eBay:iPads:: sold classification:: ebayipads_finmdl_RFE_sizes_bid1
bdanalytics
Date: (Wed) Nov 04, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 -> Rank 288 / 1884 0.85514 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.glmnet:
fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247;
prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
biddable=16.48; idseq.my=57.27;spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;
All.X.no.rnorm.rf:
fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86;
biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
cellular.fctr=6.11
All.X.no.rnorm.rf: Leaderboard: 0.77443
newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
opt.prob.threshold.OOB=0.6
startprice.diff=100.00; biddable=96.53; idseq.my=38.10;
prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
color: All.Interact.X.glmnet: fit_RMSE=88.64520; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctr:condition.fctr=77.35 D.TfIdf.sum.stem.stop.Ratio=68.18 prdline.my.fctr:color.fctr=68.12 prdline.my.fctr:storage.fctr=63.32
All.X.no.rnorm.rf: Leaderboard: 0.80638
newobs_tbl=[N=550, Y=248]; submit_filename=color_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=54]=162; max.Accuracy.OOB=0.8169492
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.diff=77.90; idseq.my=48.49;
D.ratio.sum.TfIdf.nwrds=6.48; storage.fctr=4.74;
D.TfIdf.sum.stem.stop.Ratio=4.57; prdline.my=4.32;[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.Interact.X.no.rnorm.rf: Leaderboard: 0.72974
newobs_tbl=[N=682, Y=116]; submit_filename=assctxt_Final_rf_submit
OOB_conf_mtrx=[YN=125, NY=43]=168; max.Accuracy.OOB=0.8101695; max.auc.OOB=???;
opt.prob.threshold.OOB=0.6
biddable=100.00; startprice.diff=51.04; idseq.my=29.51;
startprice.diff:biddable=28.70
prdline.my.fctriPadmini:idseq.my=6.89
Highest max.auc.OOB=???; for model: gbm w/startprice.unit9: Final.glment: min.RMSE.fit=30.32782 Ensemble.glmnet: min.RMSE.fit=29.62348 startprice.predict.All.Interact.X.no.rnorm.rf 100.000 startprice.predict.All.X.no.rnorm.rf 73.521 startprice.predict.All.Interact.X.bayesglm 29.675 startprice.predict.Max.cor.Y.lm 28.405
All.X.glmnet: min.RMSE.fit=88.64271prdl.my.descr.fctr 100.00 D.TfIdf.sum.stem.stop.Ratio 85.01 condition.fctr 80.28 carrier.fctr 77.48 prdl.my.descr.fctr:.clusterid.fctr5 65.78 D.npnct16.log 61.66 startprice.unit9 59.48 color.fctr 59.21 D.npnct01.log 53.78 D.npnct08.log 53.56 cellular.fctr 53.19
Ensemble.glmnet: Leaderboard: not submitted -> lower max.auc.OOB of "Ensemble submission"
newobs_tbl=[N=579, Y=219]; submit_filename=gbm_Final_glmnet_submit
OOB_conf_mtrx=[YN=85, NY=54]=139;
max.Accuracy.OOB=0.8438202; max.auc.OOB=0.9127314; opt.prob.threshold.OOB=0.5sold.fctr.predict.All.X.no.rnorm.rf.prob 100.0000 sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob 98.7937
Highest max.auc.OOB=0.9167568; for model:All.Interact.X.gbm biddable 100.0000 startprice.diff 96.2076 startprice.diff:biddable 23.2114 idseq.my 7.8098
mdlsel: Final.glment: min.RMSE.fit=30.47114 (higher than gbm w/startprice.unit9) Ensemble.glmnet: min.RMSE.fit=29.49418 startprice.predict.All.Interact.X.no.rnorm.rf 100.000000 startprice.predict.All.X.no.rnorm.rf 71.213880 startprice.predict.All.X.bayesglm 24.166084
All.X.glmnet: min.RMSE.fit=88.64271prdl.my.descr.fctr 100.00 D.TfIdf.sum.stem.stop.Ratio 85.01 condition.fctr 80.28 carrier.fctr 77.48 prdl.my.descr.fctr:.clusterid.fctr5 65.78 D.npnct16.log 61.66 startprice.unit9 59.48 color.fctr 59.21 D.npnct01.log 53.78 D.npnct08.log 53.56 cellular.fctr 53.19
mdlsel(startprice.log): Final.Ensemble.rf: min.RMSE.fit=0.4563772 Ensemble.rf: min.RMSE.fit=0.4283013 startprice.log.predict.All.Interact.X.no.rnorm.rf 100.0000000 startprice.log.predict.All.X.no.rnorm.rf 58.0967582 startprice.log.predict.All.Interact.X.gbm 6.7197148
All.X.no.rnorm.rf: min.RMSE.fit=1.4967021biddable 100.00000000 idseq.my 98.00292371 startprice.unit9 34.31130220 prdl.my.descr.fctr 18.10984741 D.ratio.sum.TfIdf.nwrds 15.23549621 color.fctrUnknown 14.05520993 D.TfIdf.sum.stem.stop.Ratio 13.00884673 D.ratio.nstopwrds.nwrds 10.51165302
All.X.gbm: Leaderboard: 0.75430
newobs_tbl=[N=582, Y=216]; submit_filename=mdlsel_Final_gbm_submit
OOB_conf_mtrx=[YN=58, NY=65]=123;
max.Accuracy.OOB=0.8617978; max.auc.OOB=0.9367161;
opt.prob.threshold.OOB=0.5startprice.diff 100.0000000 100.00000000 biddable 66.6475055 65.40764971 idseq.my 1.8632456 4.55963698
splogdiff: All.X.gbm: Leaderboard: 0.70111 newobs_tbl=[N=553, Y=245]; submit_filename=splogdiff_Final_gbm_submit OOB_conf_mtrx=[YN=35, NY=101]=136; max.Accuracy.OOB=0.8471910; max.auc.OOB=0.9388912; opt.prob.threshold.OOB=0.3 startprice.log.diff 100.0000000 100.0000000 biddable 86.8563123 88.0261866 idseq.my 8.3580281 2.9054298
Forum Ideas: I then focused on feature engineering, each new variable brought its own little improvement so in the end i just kept adding new ones and let the models do their thing. Here are some i used: model (productline:storage:condition), isNew, model2 (product:isNew), 50 common words from descr, descrLength, capsFactor (% of caps in description), number of cheaper items of same model2, number of dearer items of same model2, priceFactor (vs. mean of price for model), priceFactor2 (vs. mean of price for model2), bigID (if ID> 11000 because there seems to be a huge drop in sales after some time), timeline (year of product launch, reasoning is you want to spend less money on older products).
Get the median startprice for each level of productline and condition. Take the difference from startprice as a new variable. I find median works much better than the mean since startprice is not normally distributed. I also created another binary variable on whether this difference is positive or negative.
Square root startprice
scale and center all the variables except sold, including the dummies.
Prediction Accuracy Enhancement Options:
- Not repeatable experiments:
- gbm & ensembles that include gbm
- rf that includes .rnorm ??? (now .rnorm has a set seed)
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glbFeatsText
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(6) # # of cores on machine - 2
suppressPackageStartupMessages(require(caret))
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/confusionMatrix.R")
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/ggplot.R")
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glbInpMerge <- # NULL #: default
list(fnames = c("ebayipads_finmdl_bid0_sp_out.csv", "ebayipads_mdlens_bid1_sp_out.csv"))
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- FALSE # select from c(FALSE, TRUE)
glb_split_newdata_method <- NULL # select from c(NULL, "condition", "sample", "copy")
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glbObsDropCondition <- # default : NULL
"(UniqueID %in% c(NULL
, 11234 #sold=0; 2 other dups(10306, 11503) are sold=1
, 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
)) |
(productline %in% c('iPad 5', 'iPad mini Retina')) |
# (biddable != 0) # bid0_sp
(biddable == 0) # bid1_sp
"
#parse(text=glbObsDropCondition)
#subset(glb_allobs_df, .grpid %in% c(31))
glb_obs_repartition_train_condition <- NULL
# "!is.na(sold) & (sold == 1)" # : bid._sp
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "sold" #: !_sp # "startprice" # : bid._sp #
# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr" #:!_sp # "startprice.log10" :bid._sp # glb_rsp_var_raw :default
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) { # NULL
# return(raw ^ 0.5)
# return(log(1 + raw))
# return(log10(raw)) # bid._sp
# return(exp(-raw / 2))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
# glb_map_rsp_raw_to_var(tst <- c(NA, 0, 1)) # !_sp
# glb_map_rsp_raw_to_var(tst <- c(NA, 0, 2.99, 280.50, 1000.00)) # bid._sp
glb_map_rsp_var_to_raw <- function(var) { # NULL #
# return(var ^ 2.0)
# return(exp(var) - 1)
# return(10 ^ var) # bid._sp
# return(-log(var) * 2)
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # mdl_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
# User-specified exclusions
# List feats that shd be excluded due to known causation by prediction variable
glbFeatsExclude <- c(NULL
### !_sp
, "description", "descr.my", "productline"
, "startprice", "startprice.log10.predict", "sprice.predict.diff"
### bid0_sp
# , "description", "productline"
# , "sold", "startprice.log10.cut.fctr"
# # List feats that are linear combinations (alias in glm)
# , "D.terms.post.stem.n.log", "D.weight.sum"
# #, "prdl.descr.my.fctriPad4#1:.clusterid.fctr3" This does not work
# # if RFE is rated lower than Low.cor, list feats that are in RFE & not in Low.cor
# # min.RMSE.fit(RFE.X.glmnet)=0.1138888
# # D.chrs.n.log 61.12483
# # D.chrs.uppr.n.log 61.12483
# # D.ratio.wrds.stop.n.wrds.n 61.12483
# # D.terms.post.stop.n.log 61.12483
# # D.weight.post.stem.sum 61.12483
# # D.wrds.n.log 61.12483
# # D.wrds.stop.n.log 61.12483
# # D.wrds.unq.n.log 61.12483
# #, "startprice.dcm2.is9" # min.RMSE.fit(RFE.X.glmnet)=0.1141991 (up)
# , "D.wrds.stop.n.log" # min.RMSE.fit(RFE.X.glmnet)=0.1131232
### bid0_sp
### bid1_sp
# , "description", "productline"
# , "sold", "startprice.log10.cut.fctr"
### bid1_sp
)
glb_id_var <- c("UniqueID")
glb_category_var <- "prdl.descr.my.fctr" # "productline" # NULL
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glbFeatsDerive <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["prdline.my"]] <- list(
# mapfn=function(productline) { return(productline) }
# , args=c("productline"))
### bid._sp
# glbFeatsDerive[["startprice.log10.cut.fctr"]] <- list(
# mapfn=function(startprice.log10) { return(cut(startprice.log10, 3)) }
# , args=c("startprice.log10"))
### bid._sp
glbFeatsDerive[["sprice.root2"]] <- list(
mapfn = function(startprice) { return(startprice ^ (1/2)) }
, args = c("startprice"))
glbFeatsDerive[["sprice.log10"]] <- list(
mapfn = function(startprice) { return(log(startprice)) }
, args = c("startprice"))
glbFeatsDerive[["sprice.d20nexp"]] <- list(
mapfn = function(startprice) { return(exp(-startprice / 20)) }
, args = c("startprice"))
glbFeatsDerive[["sprice.predict.diff"]] <- list(
mapfn = function(startprice.log10.predict, startprice) {
spdiff <- (10 ^ startprice.log10.predict) - startprice;
return(spdiff) }
, args = c("startprice.log10.predict", "startprice"))
# glbFeatsDerive[["spdiff.root10"]] <- list(
# mapfn = function(sprice.predict.diff) {
# return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# , args = c("sprice.predict.diff"))
glbFeatsDerive[["spdiff.cut.fctr"]] <- list(
mapfn = function(sprice.predict.diff) {
return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
, args = c("sprice.predict.diff"))
#glb_allobs_df[which(glb_post_stop_words_terms_mtrx_lst[[txt_var]][, subset(glb_post_stop_words_terms_df_lst[[txt_var]], term %in% c("conditionminimal"))$pos] > 0), "description"]
glbFeatsDerive[["descr.my"]] <- list(
mapfn = function(description) { mod_raw <- description;
### bid._sp
# # This is here because it does not work with txt_map_filename
mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ",
mod_raw)
# # This should go into txt_map_filename
# mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
# # Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
#mod_raw <- grep(""", glb_allobs_df$descr.my, value = TRUE)
mod_raw <- gsub("&", "&", mod_raw);
mod_raw <- gsub("<", "<", mod_raw);
mod_raw <- gsub(">", ">", mod_raw);
mod_raw <- gsub("<br>", " ", mod_raw); # line break - add a count for it ???
mod_raw <- gsub(""", " ", mod_raw); # justification meta-character
mod_raw <- gsub("&#(0*)37;", "%", mod_raw);
mod_raw <- gsub("'", "'", mod_raw);
mod_raw <- gsub("([[:digit:]])\\.([[:digit:]])\\.([[:digit:]])",
"\\1point\\2\\point\\3", mod_raw);
mod_raw <- gsub("([[:digit:]])\\.([[:digit:]])", "\\1point\\2", mod_raw);
mod_raw <- gsub("([[:digit:]]),([[:digit:]])", "\\1\\2", mod_raw);
mod_raw <- gsub("\\b1st\\b", "first", mod_raw);
mod_raw <- gsub("\\b2nd\\b", "second", mod_raw);
mod_raw <- gsub("\\b3rd\\b", "third", mod_raw);
mod_raw <- gsub("\\b4th\\b", "fourth", mod_raw);
mod_raw <- gsub("\\.(com|COM)\\b", "dot\\1", mod_raw);
#
# # Modifications for this exercise only
# # Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
# mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b10\\.SCREEN\\b", "10\\. SCREEN", mod_raw);
mod_raw <- gsub("\\b128 gb\\b", "128gb", mod_raw);
mod_raw <- gsub("\\b16G\\b", "16GB", mod_raw);
# mod_raw <- gsub(" 16 gig ", " 16gb ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" 16 gb ", " 16gb ", mod_raw, ignore.case=TRUE);
#
# mod_raw <- gsub("\\bAccounts\\b", "Account", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bactivated\\b", "activate", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\badaptor\\b", "adapter", mod_raw);
# mod_raw <- gsub("\\baffects\\b", "affect", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bair-like\\b", "air -like", mod_raw);
mod_raw <- gsub("\\bALL-JUST\\b", "ALL -JUST", mod_raw);
mod_raw <- gsub("\\bApple's\\b", "Apple'", mod_raw);
# #mod_raw <- glb_allobs_df[c(1322), txt_var]; mod_raw
mod_raw <- gsub("\\bApple care\\b", "Applecare", mod_raw);
mod_raw <- gsub("\\bAT&T\\b", "ATT", mod_raw);
# mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bbarley", "barely", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(B|b)(EST|est) (B|b)(UY|uy)\\b", "\\1\\2\\3\\4", mod_raw);
# mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
# ignore.case=TRUE);
# mod_raw <- gsub("black\\),charger ", "black\\), charger ", mod_raw,
# ignore.case=TRUE);
# mod_raw <- gsub("\\bblacked\\b", "black", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bblemish\\b", "blemishes", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(B|b)(RAND|rand)( |-)(N|n)(EW|ew)\\b", "\\1\\2\\4\\5", mod_raw)
#mod_raw <- c("brand new", "BRAND new", "brand NEW", "BRAND NEW", "bbrand new", "brand-new", "brand newb")
mod_raw <- gsub("\\bbrokenCharger\\b", "broken Charger", mod_raw);
#
mod_raw <- gsub("\\bC-Major\\b", "C -Major", mod_raw)
# mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bcord", "cable", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bcables\\.No\\b", "cables. No", mod_raw);
# mod_raw <- gsub("\\bcables\\b", "cable", mod_raw, ignore.case=TRUE);
#
mod_raw <- gsub("\\bcare\\.The\\b", "care\\. The", mod_raw);
# mod_raw <- gsub("\\b(cared|careful|CAREFUL)\\b", "care", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\b(cases|casing)\\b", "case", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(88,187,280,1040,1098), txt_var]; mod_raw
mod_raw <- gsub("\\bCase/Cover\\b", "Case/ Cover", mod_raw);
mod_raw <- gsub("\\bCasing/Screen\\b", "Casing/ Screen", mod_raw);
# mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bchargers\\b", "charger", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bchip/crack\\b", "chip/ crack", mod_raw);
# mod_raw <- gsub("\\bchips\\b", "chip", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bCLEAN\\!LIKE\\b", "CLEAN! LIKE", mod_raw);
# mod_raw <- gsub("\\bcleanly\\b", "clean", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\b(C|c)olor(.*)s\\b", "\\1olor", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(280,1411), txt_var]; mod_raw
mod_raw <- gsub("\\bColors,models\\b", "Colors ,models", mod_raw);
# mod_raw <- gsub("\\bcompletely\\b", "complete", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(178), txt_var]; mod_raw
#
mod_raw <- gsub("\\bCONDITION..CLEAN\\b", "CONDITION ..CLEAN", mod_raw);
mod_raw <- gsub("\\bcondition,comes\\b", "condition ,comes", mod_raw);
mod_raw <- gsub("\\bcondition\\.Device\\b", "condition .Device", mod_raw);
mod_raw <- gsub("\\bconditionHas\\b", "condition Has", mod_raw);
mod_raw <- gsub("\\bcondition\\.\\.\\.like\\b", "condition ...like", mod_raw);
mod_raw <- gsub("\\bcondition\\*Minimal\\b", "condition *Minimal", mod_raw);
mod_raw <- gsub("\\bCondition-Moderate\\b", "Condition -Moderate", mod_raw);
mod_raw <- gsub("\\bcondition\\.The\\b", "condition .The", mod_raw);
mod_raw <- gsub("\\bCONDITION\\.VERY\\b", "CONDITION .VERY", mod_raw);
# mod_raw <- gsub(" (conditon|condtion|contidion|conditions)", " condition", mod_raw,
# mod_raw <- gsub("\\b(conditon|condtion|contidion|conditions)\\b", "condition", mod_raw,
# ", "\\1\\. \\2", mod_raw,
# ignore.case=TRUE);
# mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
#
# mod_raw <- gsub("\\bCONNECTED\\b", "CONNECT", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bconnects\\b", "connect", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(195, 379, 437), txt_var]; mod_raw
# mod_raw <- gsub("\\bCosmetics\\b", "Cosmetic", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bCracked/Damaged\\b", "Cracked/ Damaged", mod_raw);
mod_raw <- gsub("\\bcracksNo\\b", "cracks No", mod_raw);
#
# mod_raw <- gsub("\\b(D|d)amaged\\b", "\\1amage", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(116, 1360), txt_var]; mod_raw
# mod_raw <- gsub("\\bDays\\b", "Day", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bdefect(ive)*\\b", "defects", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(D|d)ented\\b", "\\1ent", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(ding|dinged)\\b", "dings", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bdo( +)not\\b", "dont", mod_raw);
mod_raw <- gsub("\\b(D|d)oes( +)(N|n)(O|o)(T|t)\\b", "\\1oes\\3\\5", mod_raw);
# mod_raw <- gsub("\\b(drop|drops)\\b", "dropped", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(E|e)dge\\b", "\\1dges", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bfineiCloud\\b", "fine iCloud", mod_raw, ignore.case = FALSE);
# mod_raw <- gsub(" fine.Its ", " fine. Its ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bfix\\b", "fixed", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bflaws\\b", "flaw", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bflawlessly\\b", "flawless", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
#
# mod_raw <- gsub(" functioanlity", " functionality", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bfunction(ing|ality)\\b", "functional", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" functional\\.Very little ", " functional\\. Very little ", mod_raw,
# ignore.case=TRUE);
mod_raw <- gsub("\\b([[:digit:]]+) (GB|gb)\\b", "\\1\\2", mod_raw);
mod_raw <- gsub("\\b([[:digit:]]+) gig\\b", "\\1gb", mod_raw);
mod_raw <- gsub("\\b(G|g)(EEK|eek) (S|s)(QUAD|quad)\\b", "\\1\\2\\3\\4", mod_raw);
# mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\(gray color", "\\(spacegray color", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
# ignore.case=TRUE);
mod_raw <- gsub("\\bGUARANTEES-IT\\b", "GUARANTEES -IT", mod_raw);
# mod_raw <- gsub("\\b(guarantee|guarantees)\\b", "guaranteed", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\ba handful of times\\b", "sparingly", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bhardly any\\b", "no", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bhardly ever used\\b", "sparingly used", mod_raw, ignore.case=TRUE);
#
mod_raw <- gsub("\\biCL0UD\\b", "iCLOUD", mod_raw);
mod_raw <- gsub("\\bI (CLOUD|cloud)\\b", "I\\1", mod_raw);
# mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
# ignore.case=TRUE);
mod_raw <- gsub("\\bIMEINo\\b", "IMEI No", mod_raw);
mod_raw <- gsub("\\bIMIE\\b", "IMEI", mod_raw);
# mod_raw <- gsub("\\bincluding\\b", "included", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bI pad\\b", "Ipad", mod_raw);
mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (A|a)(I|i)(R|r)\\b", "\\1\\2\\3\\4\\5\\6\\7",
mod_raw);
mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (M|m)ini\\b", "\\1\\2\\3\\4\\5ini", mod_raw);
mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (M|m)inis\\b", "\\1\\2\\3\\4\\5ini", mod_raw);
mod_raw <- gsub("\\b(IPAD|Ipad|iPad|ipad) ([[:digit:]])\\b", "\\1\\2", mod_raw);
mod_raw <- gsub("\\b(Ipadair|iPadAir|ipadair) ([[:digit:]])\\b", "\\1\\2",
mod_raw);
mod_raw <- gsub("\\b(iPadMini|iPadmini) ([[:digit:]])\\b", "\\1\\2", mod_raw);
mod_raw <- gsub("\\bI Phone\\b", "IPhone", mod_raw);
mod_raw <- gsub("\\bIS-NO\\b", "IS -NO", mod_raw, ignore.case = FALSE)
#
# mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" lightening ", " lightning ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bLightning-to-USB\\b", "Lightning- to- USB", mod_raw);
mod_raw <- gsub("\\b(L|l)(IKE|ike)( |-)(N|n)(EW|ew)\\b", "\\1\\2\\4\\5", mod_raw);
#mod_raw <- c("like new", "LIKE new", "like NEW", "LIKE NEW", "blike new", "like-new", "like newb")
mod_raw <- gsub("\\bLIKENEW!ONE\\b", "LIKENEW! ONE", mod_raw);
# mod_raw <- gsub("\\b(lock|locks)\\b", "locked", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\blots\\b", "lot", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" marks\\.Absolutely ", " marks\\. Absolutely ", mod_raw,
# ignore.case=TRUE);
# mod_raw <- gsub("\\bmarkings\\b", "marks", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bminis\\b", "mini", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bmonth\\b", "months", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(1803), txt_var]; mod_raw
mod_raw <- gsub("\\bNew-Other\\b", "New -Other", mod_raw);
# mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bnoted\\b", "note", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" oped ", " opened ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\botter box\\b", "otterbox", mod_raw);
#
# mod_raw <- gsub("\\bpackage\\b", "packaging", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bPACKAGE\\b", "PACKAGing", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(360, 1142), txt_var]; mod_raw
mod_raw <- gsub("\\bperfectlycord\\b", "perfectly cord", mod_raw);
# mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bPhysically\\b", "Physical", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\b(picture|pictured)\\b", "pictures", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bPICTURE\\b", "PICTUREs", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(184, 892), txt_var]; mod_raw
# mod_raw <- gsub("\\b[P|p]ower(ed|ing|s)\\b", "\\1ower", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" pre- owned ", " used ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bprevious\\b", "previously", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bpreviously (owned|used)\\b", "used", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(P|p)reviously(.*)(O|o)wned\\b", "\\1reviously\\3wned\\2",
mod_raw);
mod_raw <- gsub("\\b(P|p)reviously(.*)(U|u)sed\\b", "\\1reviously\\3sed\\2", mod_raw);
# mod_raw <- gsub("\\bproblem\\b", "problems", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bprotected\\b", "protector", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bprotection\\b", "protector", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bPROTECTION\\b", "PROTECTOR", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bREADiPad\\b", "READ iPad", mod_raw);
# mod_raw <- gsub(" re- assemble ", " reassemble ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bREFURB\\.", "REFURBished.", mod_raw);
# mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bright-hand\\b", "right -hand", mod_raw);
# mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
#
# mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bScratch-Free\\b", "Scratch- Free", mod_raw);
mod_raw <- gsub("\\bSCRATCHES/BLEMISHES...SCRATCHES\\b", "SCRATCHES/ BLEMISHES... SCRATCHES", mod_raw);
mod_raw <- gsub("\\bscratches,clear\\b", "scratches, clear", mod_raw);
mod_raw <- gsub("\\bScratches/Dent\\b", "Scratches/ Dent", mod_raw);
mod_raw <- gsub("\\bScratches/scuffs/nicks/scrapes\\b", "Scratches/ scuffs/ nicks/ scrapes", mod_raw);
# mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(scratchs|scratching)\\b", "scratches", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bset up\\b", "setup", mod_raw);
# mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bshowing\\b", "shows", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(shrink|SHRINK) (wrap|WRAP)", "\\1\\2", mod_raw);
# mod_raw <- gsub("\\bshuts\\b", "shut", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bSlightly\\b", "slight", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(Space|space) (G|g)r(a|e)y\\b", "\\1\\2ray", mod_raw);
# mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bsomescratches\\b", "some scratches", mod_raw);
# mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bstoring", "store", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("SWAPPA\\.COM", "SWAPPAsdotCOM", mod_raw, ignore.case=TRUE);
#
# mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(tear|TEAR)(s|S)\\b", "\\1", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(376), txt_var]; mod_raw
mod_raw <- gsub("\\b(touch|Touch|TOUCH) (screen|SCREEN)\\b", "\\1\\2", mod_raw);
# mod_raw <- gsub("\\bTURN\\b", "TURNS", mod_raw, ignore.case=FALSE);
#
mod_raw <- gsub("\\bUnlockedCracked\\b", "Unlocked Cracked", mod_raw);
# mod_raw <- gsub("\\bUNUSABLE\\b", "UNUSED", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\b(update|updates)\\b", "updated", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bupgrade\\b", "upgraded", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" uppser ", " upper ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("use*Case\\b", "use *Case", mod_raw, ignore.case = FALSE)
# mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
# ignore.case=TRUE);
#
# mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bwears\\b", "\\wear", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(167, 272), txt_var]; mod_raw
# mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(W|w)(IFI|ifi)( |-)(ONLY|only)\\b", "\\1\\2\\4", mod_raw);
mod_raw <- gsub("\\bwill( +)not\\b", "wont", mod_raw);
mod_raw <- gsub("\\byr\\b", "year", mod_raw);
mod_raw <- gsub("\\bZa(a|g)g Invisible(.*)Shield\\b", "ZaagInvisibleShield", mod_raw);
### bid._sp
return(mod_raw) }
, args = c("description"))
# To identify glb_id_vars with >=10 obs
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="\\bdoes( +)not\\b")), glbFeatsText]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="\\bipad [[:digit:]]\\b")), glbFeatsText][01:10]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][11:20]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][21:30]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][31:40]
glbFeatsDerive[["prdl.descr.my.fctr"]] <- list(
mapfn = function(productline, description) {
as.factor(paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0),
sep = "#")) }
, args = c("productline", "description"))
#print(mycreate_sqlxtab_df(glb_allobs_df, c("prdl.descr.my.fctr", "sold")))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
glb_derive_vars <- names(glbFeatsDerive)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glbFeatsPrice <- c("startprice") #: bid._sp # NULL or c("<price_var>")
# Text Processing Step: custom modifications not present in txt_munge
glbFeatsText <- c("descr.my") # NULL #
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: removePunctuation (use custom transformer to replace with space ???)
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words; Check stemming of "significant" words - any stopped words that should be stemmed with them ?
if (!is.null(glbFeatsText)) {
require(tm)
glb_txt_stop_words[["descr.my"]] <- sort(c(NULL
### bid._sp
# , setdiff(removePunctuation(stopwords("english")), "no")
# ,"ac"
# # cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"128gb,1st,32gb,3g,64gb,90,acceptable,activation,amount,average,bad,buttons,buy,came,camera,can,care,carrier"
# #,casing
# ,"certified,charge,charging,cleaned,clear,come,components,contain,corner,correctly,covered,customer,earbuds"
# ,"engraved,engraving,engravement" # somehow didn't show up in the cor.y.train == NA list
# ,"entire,except,fair,features,feel,fine,generation,get,gift,got,heavily,heavy,however,imei,include,inspected,invisible,invisibleshield"
# ,"ipad,ipads"
# ,"issues"
# #,items,
# ,"keyboard,lightning,listing,little,looks,lower"
# ,"manufacture,manufacturer"# somehow didn't show up in the cor.y.train == NA list
# ,"meaning,model,near,need,needs,nicks,opened,operational,otherwise"
# ,"person,personal"# somehow didn't show up in the cor.y.train == NA list
# ,"phone,photos,pics,plastic,port,professionally"
# ,"purchased,purchasing"# somehow didn't show up in the cor.y.train == NA list
# ,"quality,questions,read,ready"
# ,"receive,received"# somehow didn't show up in the cor.y.train == NA list
# ,"removed,replaced,retail,return,returns,runs"
# #,scratch,
# ,"scuffing,sealed,sell,seller,selling,shape,ship,shown,silver,since,sold,sound,spacegray,stock,sync,tablet,taken,technician,tests,third,time,touch,units,unlocked,week,wifi,without"
# ,"wrap" # somehow didn't show up in the cor.y.train == NA list
# ,"zagg"
# ), collapse=",")
# , "[,]")) #err.abs.fit.sum=26.869473 w/o items,scratch
#
# # cor.y.abs is low
# #,"always","comes","grade","moderate","protector"
### bid._sp
### !_sp
# freq == 1; keep "gold"
,"09","17","28","34","360","5c","5point1point1","511","6428"
,"7point9","7point9in","79in"
,"8point5","8point25","82510"
,"910","9510","9point7","9point75","97510","99"
,"a1314","a1430","abused","accept","accounts","across","actuuly","add","advised"
,"affects","am","ans","antenna","anti","anyone","anything","applied","applying"
,"area","arizona","att","attached"
,"backlight","backlit","backplate","barley"
,"beetle","beginning","bend","besides","between"
,"bidder","binder","bonus","book","boot","bound","brick","broke","bruises"
,"buyers"
,"capacity","causing","cherished","chrome","classes","closely"
,"condtion","conditon","confidence","considerable","consist","consistent"
,"consumer","contents","contidion","control"
,"couldnt"
,"cream","customize","cuts"
,"daily","date","daughter"
,"deactivated","decent","deep","defender","defense","degree","demonstration"
,"depicted","depress","desciption"
,"difficulty","digi","disclaimer","discoloration","distressed","divider"
,"dlxnqat9g5wt","dock","documents","done","durable","dust","duty"
,"each","effect","either","emblem","erased","esi","essentially","etch","etched"
,"euc","every","exact","exhibition","expires"
,"facing","faded","faint","february","film","final","five","flickers"
,"folding","forgot","forwarders"
,"freezes","freight"
,"functioanlity"
,"games","generic","genuine","glitter","goes","gray","grey","guide"
,"hairline","half","handstand","hdmi","here","high","higher"
,"hold","hole","hospital"
,"immaculate","impact"
,"inivisible","instead"
,"intended","interest","interior","international","internationally"
,"into","intro"
,"ios","isnt","itself","ive"
,"jack","july"
,"keyword","kids","kind","knicks"
,"l","largest","last","late","length","let","letters","level"
,"lifting","limited","literally","literature"
,"local","logic","long","longer","looping","loose","loss","lost"
,"mars"
,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll"
,"mic","middle","mind","minimum","mixed"
,"myself"
,"neither","newer","non","none","nonviewing","nor","november"
,"occasional","oem","often","online","oped","outside","over"
,"padfolio","pairing","paperwork","past","period","pet","piece"
,"plate","played","plug"
,"poor","portfolio","portion","pouch"
,"preinstalled","pressure","price","proof","provided"
,"ranging","rather"
,"real","realized","reassemble","reboot","receipt","recently","red"
,"reflected","refunds","relisting","remote","repeat","reponding"
,"required"
,"reserve","reshaped","residue","respond","restarts","result"
,"reviewed"
,"ringer","roughly","rubber"
,"said","same","school","scratchs","screeb"
,"seamlessly","seem","seen","semi","send","september","serious"
,"shell","shipment","short","showroom"
,"sighs","site","size"
,"sleeve","slice"
,"smoke","smooth","smudge","sn"
,"softer","software","somewhat","soon"
,"space","sparingly","sparkiling","spec","special","speck","speed","speigen"
,"stains","standup","start","status","stopped","strictly"
,"subtle","sustained"
,"swappadotcom","swiped","swivel"
,"take","technical","tempered","texture"
,"thank","then","therefore","think","those","though"
,"toddler","topback","totally","touchy","toys"
,"tried","turn","typical"
,"u","university","unknown","untouched","uppser"
,"valid","vary","viewing","virtually"
,"want","wavy","website","whole","why","winning","worn"
,"zaag","zero","zombie","zoogue"
# cor.y.train == NA
,"account","applecare","download","expect","fourth","greeting","maybe"
,"plus","purposes","significant","title","volume"
# chisq.pval high (e.g. == 1);
# keep
# carrier.fctr: "sprint", "verizon"
# cellular.fctr: "3g", "4g",wifion
# color.fctr: "gold"
# prdl.descr.my.fctr:
# storage.fctr: "128gb"
,"2016"
,"acceptable","actual","amount","awesome"
,"beautiful","before","bent","best","blocked","blocks"
,"capable","converted"
,"find"
,"gift"
,"handled","handling","headphone"
,"im","information"
,"love"
,"march","meaning","means","medium","money"
,"necessary"
,"offer","once"
,"page","product","products"
,"second","seconds","should","silver","skin","skinned"
,"tape","thoroughly","twice"
,"user"
,"way","which"
# nzv.freqRatio high (e.g. >= glb_nzv_freqCut);
# keep
# carrier.fctr: "sprint", "tmobile", "verizon"
# cellular.fctr: "3g", "4g",wifionly
# color.fctr: "gold",spacegray
# condition.fctr:
# levels:
# "Used", "For parts or not working", "Manufacturer refurbished", "New", "New other (see details)", "Seller refurbished"
# stemmed tokens:
# manufactur
# prdl.descr.my.fctr: "ipad1",ipad3,ipad4,ipadair2,ipadmini2
# storage.fctr: "128gb"
,"14","2015","3","30","4","5","6","7","8","9","90","9point5","9point7in"
,"a1432","able","about","ac","activate","activated","activation"
,"additional","adult"
,"after"
,"ago"
,"air"
,"along","already","also"
,"another","answer"
,"appears","approved","april"
,"around"
,"asis","associated"
,"auction"
,"backside","bad","battery","because","bestbuy","bezel","blue","bluetooth"
,"board","body","both","bottom","bought"
,"bright"
,"bumps","buy","buying"
,"came","camera","cameras","cannot","cant","card","carrier","cellular"
,"changed","changing","check","chip","chips"
,"color","colors","company","complete","completely","components"
,"connect","connected","connector","connects","contain","contains"
,"corporate","correctly","couple"
,"customer"
,"data"
,"dead"
,"default","defaulting","definite","definitely"
,"delivered","demo","describe","described","details"
,"do","does","dont","down"
,"drop","dropped","drops"
,"due"
,"earbuds","easily","ebay","else","engraved","engravement","engraving","entire"
,"etc"
,"even","ever","everything"
,"except","exterior","extremely"
,"fantastic","fast","faulty","features","feel","feels","fine","fix","fixed"
,"flaw","flaws"
,"frame","framing"
,"geeksquad","general","get","got","guarantee","guaranteed","guarantees"
,"hand","handful","handset","happened","hard","hardly","heavy","home","however"
,"id","if","images","imperfections"
,"inside","inspected","install","installed","instructions","invisible"
,"iphone","iphones"
,"issue","issues"
,"itunes"
,"keyboard","know","known"
,"latest","lcd","least","leather","less"
,"life","lightening","lightning","like","line","lining"
,"liquid","liquidation"
,"logo","lot","lots","lower"
,"magnetic","make","manual","manuals","many","me","memory","missing"
,"model","models","moderate","more","most","mostly"
,"multiple","musthave"
,"my"
,"name","network","networks","nice","nick","nicks"
,"nonfunctioning","noticeable","now"
,"off","old","operated","operational","otherwise","outer","own","owned"
,"party","passcode","password"
,"performance","performs","person","personal","personalized"
,"phone","physical","physically"
,"pin","pixels"
,"placed","places"
,"port","possible"
,"pretty","pristine","problem","problems","properly"
,"purchase","purchased","purchasing"
,"quality","question","questions"
,"rarely"
,"reactivated","really","rear","receive","received","regular","removed"
,"repair","retail","retina"
,"rotate","rotation"
,"running","runs"
,"s","sale","sales","sameday","scrapes","scroll","setup","several"
,"shattered","shrinkwrap","shut","shuts"
,"side","sides","sim","single"
,"so","something","sometimes","sound"
,"speaker","specifics","spent"
,"sticker","stickers","storage","store","storing","stuck","stylus"
,"super","supply","sure","surface"
,"sync"
,"taken","technician","than","these","they","thin","third","three"
,"till","tiny"
,"too","took","touch","touching","touchscreen"
,"turns"
,"two"
,"unable","under","unnoticeable","unopened","unsealed","until"
,"unused","unusable"
,"up","update","updated","updates","upgrade","upgraded","upper"
,"usage","usually"
,"verified","verify"
,"wall","water","we","week","weeks", "were","what","whats","where","while"
,"wiped","without"
,"would"
,"wrap","wrapped","wrong"
,"x"
,"year","years","your"
,"zagg","zaaginvisibleshield"
#
### !_sp
))
}## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^2", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glb_allobs_df[glb_post_stem_words_terms_mtrx_lst[[txt_var]][, 6] > 0, glbFeatsText]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq <= 2)$term), collapse = ",")
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (freq <= 2)))
#glb_allobs_df[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txt_var]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txt_var]]), 5)
#glb_allobs_df[glb_post_stem_words_terms_mtrx_lst[[txt_var]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^m", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txt_var]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (nzv.freqRatio >= glb_nzv_freqCut) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txt_var]]), 20)
#mydsp_obs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glb_allobs_df[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glb_category_var, "storage", txt_var)]
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txt_var]][grep("^moder", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txt_var]][grep("^came$", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txt_var]][glb_allobs_df$.lcn == "Fit", term_row_df$pos], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txt_var]][grep("condit", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^con", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glb_allobs_df[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glb_id_var, "productline", txt_var)]
#glb_allobs_df[which(TfIdf_stem_mtrx[, 191] > 0), c(glb_id_var, glb_category_var, txt_var)]
#which(glb_allobs_df$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txt_var]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^c", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glb_allobs_df$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glb_txt_corpus_lst[[txt_var]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glb_txt_synonyms <- list()
glb_txt_synonyms[["descr.my"]] <- #NULL : default
list(NULL
### bid._sp
# , list(word="cabl", syns=c("cabl", "cord"))#err.abs.fit.sum=26.863220
# # , list(word="charger", syns=c("charg", "charger"))
# # , list(word="come", syns=c("came", "come"))
# # , list(word="dent", syns=c("dent", "ding"))
# # , list(word="damag", syns=c(#"bad", "blemish", "broken", "crack",
# # #defect has +ve cor, others have -ve cor
# # "damag", "dent", "ding",
# # #"scratch", "scuff", "tear", "wear",
# # NULL))
# # # combining damag with defect & dent results in higher err.abs.fit.sum=26.885899
# # # combining defect with dent in higher err.abs.fit.sum=26.894976
# # , list(word="defect", syns=c(#"bad", "blemish", "broken", "crack",
# # "defect", "dent", #"ding", ding has -ve cor, others have +ve cor
# # #"scratch", "scuff", "tear", "wear",
# # NULL))
# #, list(word="new", syns=c("brand")) ???
# # , list(word="scuff", syns=c("scuf", "scuff"))
# # , list(word="show", syns=c("show", "shown"))
# # , list(word="tablet", syns=c("tab", "tablet"))
### bid._sp
### !_sp
,list(word = "cabl", syns = c("cabl", "cord"))
,list(word = "cant", syns = c("cant", "cannot"))
,list(word = "descript", syns = c("descript", "discript"))
,list(word = "generat", syns = c("gen", "generat"))
,list(word = "ipadmini", syns = c("ipadmini", "mini"))
,list(word = "kept", syns = c("keep", "kept"))
,list(word = "know", syns = c("know", "known"))
,list(word = "lightn", syns = c("lighten", "lightn"))
,list(word = "passcod", syns = c("passcod", "password"))
,list(word = "photo", syns = c("photo", "photograph", "photos", "pic", "pictur"))
,list(word = "preown", syns = c("pre", "preown", "previous",
"previouslyus", "previouslyown"))
,list(word = "protector", syns = c("protect", "protector"))
,list(word = "scuff", syns = c("scuf", "scuff"))
,list(word = "tablet", syns = c("tab", "tablet"))
,list(word = "with", syns = c("w", "with"))
,list(word = "zagg", syns = c("zagg", "zaaginvisibleshield"))
### !_sp
)
if (length(glb_txt_synonyms) > 0) names(glb_txt_synonyms) <- glbFeatsText
# Text Processing Step: filterTerms
if (!is.null(glbFeatsText)) {
require(tm)
# options include: weightTf, myweightTflog1p, myweightTfsqrt, weightTfIdf, weightBM25
glb_txt_terms_control <- list(weighting = weightTfIdf # : default
# termFreq selection criteria across obs: default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf)) # bid._sp: list(global=c(3, Inf))
# default: c(3, Inf)
, wordLengths = c(1, Inf) # bid._sp: c(2, Inf)
)
}
glb_txt_cor_var <- glb_rsp_var # bid._sp: "startprice.log10.cut.fctr" # default: glb_rsp_var
# select one from c("union.top.val.cor", "top.cor", default: "top.val", "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- c(15) # bid._sp: c(20) # c(50) in (old) !_sp # default: rep(10, length(glbFeatsText))
names(glbFeatsTextTermsMax) <- glbFeatsText
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- c(0.45) #bid._sp: c(0.4) #(old) !_sp: 0.2 #default: rep(1, length(glbFeatsText))
names(glbFeatsTextAssocCor) <- glbFeatsText
# Text Processing Step: extractPatterns (ngrams)
# Potential Enhancements
# "Seller refurbished" -> D.P.refurbished.seller ?
# "Like new" -> D.P.new.like ?
# "No scratches" -> D.P.scratch.no ?
glb_important_terms <- list()
# Remember to use stemmed terms
# Have to set it even if it is not used
glb_sprs_thresholds <- c(0.950) # Generates 8 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glbFeatsText
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude,
glb_rsp_var_raw)
glbFctrMaxUniqVals <- 23 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE # bid._sp:TRUE # default:FALSE
glb_cluster.seed <- 189 # or any integer
### !_sp
glb_cluster_entropy_var <- glb_rsp_var # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
### bid._sp
# glb_cluster_entropy_var <- "sold" #"startprice.log10.cut.fctr"
# glbFeatsTextClusterVarsExclude <- TRUE # default FALSE
### bid._sp
glb_interaction_only_feats_lst <- list()
### bid._sp
glb_interaction_only_feats_lst[["carrier.fctr"]] <- "cellular.fctr"
glb_nzv_freqCut <- 19 # 19 is caret default
glb_nzv_uniqueCut <- 10 # 4 : bid._sp # 10 : caret::default
### bid._sp
glb_rfe_fit_sizes <- # NULL
### bid0_sp
# c(106, 111, 116, 120, 128) # or NULL c(8, 16, 32, 64, 128, 140)
### bid1_sp
# c(8, 11, 16, 21, 32, 64, 128)
c(47,48,49,50,51,52,53,54)
# outliers identified by car::outlierTest
glb_obsfit_outliers <- #NULL
### bid._sp
# c(NULL # default: NULL
# biddable == 0 & 1; err.abs.fit.sum=423.55172
# # outliers
# , 10813 # next 665 w/ rstudent=-5.091080; biddable=3.263257; err.abs.fit.sum=418.598755
# , 10666 # next 1727 w/ rstudent=-5.163517; biddable=4.293465; err.abs.fit.sum=414.093609
# , 11736 # next 780 w/ rstudent=-5.181343; biddable=5.670483; err.abs.fit.sum=401.817992
# # old biddable importance above this
# , 10781 # next 1323 w/ rstudent=-5.151062; biddable=13.30602; err.abs.fit.sum=396.393721
# #, 10091 # next 91 w/ rstudent=-4.444452; biddable=; err.abs.fit.sum=402.673715 (up)
# #, 10166 # next 560 w/ rstudent=-5.006795; biddable=; err.abs.fit.sum=401.759324 (up)
# #, 10281 # next 281 w/ rstudent=-4.245087; biddable=; err.abs.fit.sum=401.316926 (up)
# #, 10285 # next 285 w/ rstudent=-4.483190; biddable=; err.abs.fit.sum=402.608936 (up)
# #, 10446 # next 445 w/ rstudent=-4.663418; biddable=; err.abs.fit.sum=403.074523 (up)
# #, 10542 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205 (up)
# #, 10543 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205 (up)
# #, 10561 # next 542 w/ rstudent=-4.736154; biddable=; err.abs.fit.sum=401.56198 (up)
# #, 10631 # next 166 w/ rstudent=-5.073048; biddable=; err.abs.fit.sum=401.556788 (up)
# #, 11330 # next 630 w/ rstudent=-5.117659; biddable=; err.abs.fit.sum=401.732597 (up)
# , 10091, 10166, 10281, 10285, 10446, 10542, 10543, 10561, 10631, 11330
# # biddable=18.93923; err.abs.fit.sum=359.388769
# , 10330 #biddable=19.06084; err.abs.fit.sum=355.895702
# , 10402 #biddable= 0.0 ; err.abs.fit.sum=351.315181
# , 10438 #biddable= 0.0 ; err.abs.fit.sum=347.821527
# , 10624 #biddable= 0.0 ; err.abs.fit.sum=343.724904
# , 10659 #biddable= 0.0 ; err.abs.fit.sum=331.873603
# , 11323 #biddable=10.45901; err.abs.fit.sum=324.929562
# , 11422 #biddable= 0.0 ; err.abs.fit.sum=334.839805 (up)
# biddable == 0; err.abs.fit.sum=26.713317
# , 11448 # outliers; next is 858 w/ rstudent=-5.855132; err.abs.fit.sum=24.212800
# , 11583 # outliers; next is 856 w/ rstudent=-4.792849; err.abs.fit.sum=22.164035
# , 11581 # outliers; next is 743 w/ rstudent=-4.005054; err.abs.fit.sum=18.842901
# , 10837 # outliers; next is 336 w/ rstudent=-5.279215; err.abs.fit.sum=18.124560
# , 11442 # outliers; next is 904 w/ rstudent=-4.474844; err.abs.fit.sum=15.533211
# , 11697 # outliers; next is 874 w/ rstudent=-3.678664; err.abs.fit.sum=13.829375
# , 10799 # .hatvalues == 1; total 8; iPadmini#1; err.abs.fit.sum=13.807283
# , 10017 # .hatvalues == 1; total 7; iPad3#1; err.abs.fit.sum=14.620782 (up)
# , 10027, 10859 # .hatvalues == 1; total 7; iPad1#1; err.abs.fit.sum=14.570246 (up)
# , 10332 # .hatvalues == 1; total 7; iPad4#1; err.abs.fit.sum=13.706467
# , 11759 # .hatvalues == 1; total 6; iPadAir2#1; err.abs.fit.sum=13.643043
# , 10675 # .hatvalues == 1; total 5; iPadAir#1; err.abs.fit.sum=13.623787
# , 11119 # .hatvalues == 1; total 4; iPadmini3#1; err.abs.fit.sum=NA
# , 10017, 10027, 10859 # .hatvalues == 1; total 1; iPad3#1 & iPad1#1; err.abs.fit.sum=13.438903
# biddable == 1; err.abs.fit.sum=361.78243
# , 10813 # outliers; next is 665 w/ rstudent=-5.021180; err.abs.fit.sum=356.83424
# , 10666 # outliers; next is 808 w/ rstudent=-4.764126; err.abs.fit.sum=352.46437
# , 11736 # outliers; next is 665 w/ rstudent=-4.614022; err.abs.fit.sum=348.59977
# , 10542 # outliers; next is 665 w/ rstudent=-4.654923; err.abs.fit.sum=344.18546
# , 11330 # outliers; next is 327 w/ rstudent=-4.628972; err.abs.fit.sum=336.12636
# , 10561 # outliers; next is 56 w/ rstudent=-4.612970; err.abs.fit.sum=329.50309
# , 10166 # outliers; next is 318 w/ rstudent=-4.717238; err.abs.fit.sum=318.50562
# , 10543 # outliers; next is 464 w/ rstudent=-4.811116; err.abs.fit.sum=314.32801
# , 10285 # outliers; next is 21 w/ rstudent=-4.850822; err.abs.fit.sum=310.19008
# #, 10091 # outliers; next is 464 w/ rstudent=-4.941448; err.abs.fit.sum=312.94069 (up)
# #, 10781 # outliers; next is 250 w/ rstudent=-4.793502; err.abs.fit.sum=313.03867 (up)
# , 10446 # outliers; next is 371 w/ rstudent=-4.787578; err.abs.fit.sum=307.15681
# , 10631 # outliers; next is 165 w/ rstudent=-4.130356; err.abs.fit.sum=303.34549
# #, 10330 # outliers; next is 217 w/ rstudent=-4.067684; err.abs.fit.sum=312.75121 (up)
# #, 10402 # outliers; next is 388 w/ rstudent=-4.067684; err.abs.fit.sum=311.84516 (up)
# #, 10659 # outliers; next is 128 w/ rstudent=-3.982911; err.abs.fit.sum=311.84516 (up)
# , 10091, 10781, 10330, 10402, 10659#, 10281 outliers; err.abs.fit.sum=282.381827; iPad4#0=13.806011; iPad4#1=7.799398
# #, 10281 # outliers; next is NA w/ rstudent=NA; err.abs.fit.sum=287.147331 (up); iPad4#0=14.372770; iPad4#1=4.591408
# #, 10624 # outliers; ignored along with 10281 err.abs.fit.sum=289.116467 (up); iPad4#0=; iPad4#1=
# #, 10624 # outliers; ignored w/o 10281 err.abs.fit.sum=286.415040 (up); iPad4#0=; iPad4#1=
# #, 10636 # hatvalues==1; next is 11652; err.abs.fit.sum=290.50254 (up)
# , 11652 # hatvalues==1; next is 10636; err.abs.fit.sum=282.183867
# #err.abs.fit.sum=282.227249
# )
### bid._sp
# .hatvalues == 1
c(10096, 11062, 11233, 11660, 11768)
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#print(outliers <- car::outlierTest(glb_models_lst[["RFE.X.glm"]]$finalModel))
#print(outliers_df <- data.frame(.Bonf.p=outliers$bonf.p))
#obs_df <- glb_trnobs_df; mdlId <- "RFE.X.Train.glm"
#model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)))
#print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")]))
#print(subset(model_diags_df, is.na(.dffits)))
#print(subset(model_diags_df, .hatvalues == 1))
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glb_fitobs_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#indep_vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(glb_sel_mdl_id, ".importance"))), glb_featsimp_df))); indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(model_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glb_category_var] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#table(glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), c(glb_id_var, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glb_id_var, category_var=glb_category_var)
#dffits_ctgry_df <- subset(dffits_df, prdl.descr.my.fctr %in% c("Unknown#0"))
#myplot_parcoord(obs_df=dffits_ctgry_df[, c(glb_id_var, glb_category_var, ".dffits", ".Bonf.p", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:5])], obs_ix=seq(1:nrow(dffits_ctgry_df))[!is.na(dffits_ctgry_df$.Bonf.p)], id_var=glb_id_var, category_var=glb_category_var)
#
#car::influenceIndexPlot(glb_models_lst[["RFE.X.glm"]]$finalModel, id.n=3)
# myplot_parcoord(obs_df=glb_fitobs_df[, c(glb_id_var, glb_rsp_var,
# "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:5])], obs_ix=hatobs_ix, id_var=glb_id_var)
# myplot_parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:2])], obs_ix=hatobs_ix)
# hatvals <- hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel)
# hatobs_ix <- which(hatvals == max(hatvals))
# MASS::parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:2])], var.label=TRUE)
#plot(hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel), type = "h")
#glb_fitobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]
#all.equal(glb_models_lst[[glb_sel_mdl_id]], glb_models_lst[[glb_fin_mdl_id]])
glb_obstrn_outliers <- c(glb_obsfit_outliers
)
#car::outlierTest(glb_models_lst[["RFE.X.glm"]]$finalModel)
#glb_trnobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm",
, "glm"
#, "bayesglm" # crashing w/ parallel processing
, "glmnet", "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
#, "nnet" , "avNNet" # predicts 1 for all obs in bid0_sp # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
#, "svmPoly" # crashing w/ parallel processing #, "svmPoly" runs 75 models per cv sample for tunelength=5
#, "svmRadial" # crashing w/ parallel processing
, "earth"
#, "bagEarth" # Takes a long time
)
} else
# Classification - Add ada,bagEarth (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "glm"
, "bayesglm" # crashing w/ parallel processing
, "glmnet", "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
#, "svmLinear" # crashing with interaction variables
#, "svmLinear2" # crashing with interaction variables
#, "svmPoly" # crashing with interaction variables #, "svmPoly" runs 75 models per cv sample for tunelength=5
#, "svmRadial" # crashing with interaction variables
, "earth"
#, "bagEarth" # Takes a long time
) else
glbMdlMethods <- c("rpart", "rf", "gbm")
glb_mdl_family_lst <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
glb_mdl_family_lst[["RFE.X"]] <- glbMdlMethods # non-NULL list is mandatory
glb_mdl_family_lst[["All.X"]] <- "glmnet" # non-NULL list is mandatory
glb_mdl_family_lst[["Best.Interact"]] <- "glmnet" # non-NULL list is mandatory
### bid1_sp
# glb_mdl_family_lst[["CSM.X"]] <- "glmnet"
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# # from RFE.X
# , "startprice.dgt1.is9", "startprice.dcm2.is9", "startprice.dcm1.is9", "startprice.dgt2.is9"
# #, "condition.fctr"
# , "prdl.descr.my.fctr", "color.fctr"
# #, "D.ratio.weight.sum.wrds.n"
# , "cellular.fctr", "cellular.fctr:carrier.fctr"
#
# # from RFE.X.Interact
# , "cellular.fctr:prdl.descr.my.fctr", "cellular.fctr:startprice.dgt2.is9", "cellular.fctr:startprice.dgt1.is9", "cellular.fctr:color.fctr"
# , "cellular.fctr:condition.fctr" # RMSE up with keeping condition.fctr in the model
# # RMSE & R.sq up with removing condition.fctr from the model
# , "cellular.fctr:D.ratio.weight.sum.wrds.n"
# )
### bid1_sp
### !_sp
# glb_mdl_family_lst[["CSM.X"]] <- "glmnet"
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio",
# , "D.npnct15.log", "D.npnct03.log", "D.wrds.n.log", "D.chrs.n.log")
# indep_vars <- union(setdiff(indep_vars, interact_vars_vctr),
# paste(glb_category_var, interact_vars_vctr,
# sep=ifelse(grepl("\\.fctr", glb_category_var), "*", ".fctr*")))
# indep_vars <- union(setdiff(indep_vars,
# c("startprice.log.diff", "startprice.unit9", "biddable", "cellular.fctr", "carrier.fctr")),
# c("startprice.log.diff*biddable", "startprice.unit9*biddable", "cellular.fctr*carrier.fctr"))
### !_sp
# Check if interaction features make fit better
# Check if tuning parameters make fit better
glb_tune_models_df <- data.frame()
#RFE.X.avNNet
### bid0_sp
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
### bid1_sp
# size=[1] 3 5 7 9; decay=0 0.0001 [0.001] 0.01 0.1; bag=[FALSE]; RMSE=0.9285472
### bid0&1_sp
#RFE.X.bagEarth
### bid0_sp
#RFE.X.bagEarth degree=[1]; nprune=[33]; RMSE=0.1507259
### bid1_sp
#RFE.X.bagEarth degree=[1]; nprune=[32]; RMSE=0.6379639
#RFE.X.bagEarth degree=[1] 2 3; nprune=8 16 32 64 [128]; RMSE=0.6334405
#RFE.X.bagEarth degree=1 [2]; nprune=16 32 64 128 [256]; RMSE=0.6211924
#RFE.X.bagEarth degree=1 [2]; nprune=64 128 200 225 [256]; RMSE=0.6320776 (up)
#RFE.X.bagEarth degree=[1] 2; nprune=64 128 225 256 [275]; RMSE=0.640644 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 200 [256] 300; RMSE=0.6496039 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=32 64 128 256 [512]; RMSE=0.6404529 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
### bid0&1_sp
### bid0_sp
#RFE.X.earth degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
### bid0_sp
#RFE.X.gbm
### bid0_sp
# shrinkage=[0.1]; n.trees=50 100 150 [200] 250; RMSE=0.2062651
# shrinkage=0.00 0.05 0.10 0.15 [0.20]; n.trees=50 [100] 150 200 250; interaction.depth=1 [2] 3 4 5; n.minobsinnode=[10]; RMSE=0.2019453
# shrinkage=0.00 0.05 [0.10] 0.15 0.20; n.trees=50 100 150 200 [250]; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
### bid1_sp
# shrinkage=[0.1]; n.trees=50 100 150 200 [250]; interaction.depth=1 2 3 4 [5]; n.minobsinnode=[10]; RMSE=0.5054172
# shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 [150] 200 250 300; interaction.depth=2 3 4 5 [6]; n.minobsinnode=6 [8] 10 12 14; RMSE=0.5036430
# shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 150 [200] 250 300; interaction.depth=3 4 5 [6] 7; n.minobsinnode=6 8 [10] 12 14; RMSE=0.502774
# shrinkage=0.04; n.trees=200; interaction.depth=6; n.minobsinnode=10; RMSE=0.502774
# shrinkage=[0.05] 0.10 0.15 0.20 0.25; n.trees=100 [150] 200 250 300; interaction.depth=2 3 [4] 5 6; n.minobsinnode=[10]; RMSE=0.5058678 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", vals = "0.04")
# ,data.frame(method = "gbm", parameter = "n.trees", vals = "200")
# ,data.frame(method = "gbm", parameter = "interaction.depth", vals = "6")
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", vals = "10")
# ))
### bid0&1_sp
#RFE.X.glmnet
### bid1_sp
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
### bid1_sp
#RFE.X.nnet
### bid0_sp
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; RMSE=1.3300906
### bid1_sp
# size=1 3 5 7 [9]; decay=0e+00 1e-04 1e-03 1e-02 [1e-01]; RMSE=0.9289109
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
### bid0&1_sp
#RFE.X.rf
### bid0_sp
# mtry=2 35 [68] 101 134; RMSE=0.1331992
# mtry=2 35 68 [101] 134; RMSE=0.1339974
### bid0_sp
# mtry=2 41 81 121 [161]; Accuracy=0.8398314
# mtry=41 81 [121] 161 181; Accuracy=0.8282403
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "41 81 121 141 161")
# ))
#RFE.X.rpart
### bid0_sp
# cp=[0.03230142] 0.06012801 0.09395662 0.12251081 0.35258370; RMSE=0.1771138
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="rpart", parameter="cp", min=0.02, max=0.04, by=0.005)
# ))
### bid1_sp
# cp=[0.008081388] 0.016191995 0.027590245 0.299848193 0.361621486; RMSE=0.5294398
# cp=[0.005] 0.006 0.007 0.008 0.009 0.010; RMSE=0.522678
# cp=0.001 [0.003] 0.005 0.007 0.009; RMSE=0.5186586
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="rpart", parameter="cp", min=0.001, max=0.010, by=0.002)
# ))
### bid0&1_sp
#RFE.X.svmLinear
### bid0_sp
# C=[1]; RMSE=0.1374094
# C=1e-02 [0.1] 5e-01 1e+00 2e+00 3e+00 4e+00 1e+01 1e+02; RMSE=0.1271318
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
### bid1_sp
# C=[1]; RMSE=0.6614060
# C=1e-02 [1e-01] 1e+00 1e+01 1e+02; RMSE=0.6373977
# C=[0.05] 0.10 0.50 1.00 10.00; RMSE=0.6324697
# C=0.01 [0.05] 0.10 0.50 1.00; RMSE=0.6324697
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
### bid0&1_sp
#RFE.X.svmLinear2
### bid0_sp
# cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.1276354
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
### bid1_sp
# cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.6483622
# cost=[0.0625] 0.1250 0.25 0.50 1.00; RMSE=0.6335311
# cost=0.0312 [0.0625] 0.1250 0.25 0.50; RMSE=0.6335311
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0312 0.0625 0.125 0.25 0.50")
# ))
### bid0&1_sp
#RFE.X.svmPoly
### bid0_sp
# degree=[1] 2 3; scale=0.001 0.01 [0.1] 1 10; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1276130
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ))
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
### bid0_sp
#RFE.X.svmRadial
### bid0_sp
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
### bid0_sp
#data.frame(parameter="mtry", min=080, max=100, by=10),
#glb_to_sav(); all.equal(sav_models_df, glb_models_df)
#glb_models_df <- subset(sav_models_df, id != "RFE.X.rf"); print(sort(glb_models_df$id))
glb_preproc_methods <- #NULL
### bid0_sp
# c("YeoJohnson", "center.scale",
# # crashes with train: all the RMSE metric values are missing
# # probably due to interaction vars
# "range", "pca", "ica",
# "spatialSign")
### bid0_sp
### bid1_sp
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
### bid1_sp
c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.AUCROCR.OOB", "max.AUCpROC.OOB", "max.Accuracy.OOB", "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- #"auto"
### bid0_sp
# c("RFE.X.glm"
# #, "RFE.X.bayesglm"
# , "RFE.X.glmnet", "RFE.X.rpart", "RFE.X.gbm", "RFE.X.rf", "RFE.X.svmLinear", "RFE.X.svmLinear2"
# #, "RFE.X.svmPoly", "RFE.X.svmRadial"
# , "RFE.X.earth", "RFE.X.bagEarth", "RFE.X.Interact.glmnet", "RFE.X.YeoJohnson.glmnet", "RFE.X.center.scale.glmnet", "RFE.X.spatialSign.glmnet")
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
# c("RFE.X.spatialSign.rf", "RFE.X.YeoJohnson.rf", "RFE.X.center.scale.rf", "RFE.X.rf", "RFE.X.avNNet", "RFE.X.bagEarth", "RFE.X.earth", "RFE.X.gbm", "RFE.X.glmnet", "RFE.X.nnet", "RFE.X.svmLinear2", "RFE.X.glm", "RFE.X.svmLinear", "RFE.X.rpart")
### bid1_sp
c("RFE.X.rpart", "RFE.X.earth", "RFE.X.glmnet", "RFE.X.glm", "RFE.X.bayesglm", "RFE.X.nnet", "RFE.X.avNNet", "RFE.X.Interact.glmnet", "RFE.X.gbm"
#,"RFE.X.bagEarth" #takes a long time
)
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glb_fitobs_df[, grep(paste0("^", gsub(".", "\\.", glb_rsp_var_out, fixed = TRUE), "RFE\\.X\\.(.*)\\.prob"), names(glb_fitobs_df), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[[glb_sel_mdl_id]])$importance))[1], ]); summary(cor_vctr); cor_vctr
glb_sel_mdl_id <- "Ensemble.glmnet" #select from c(NULL, "All.X.glmnet", "RFE.X.glmnet", "Ensemble.auto.glmnet", "Ensemble.glmnet")
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c("sold", ".grpid", "color", "condition", "cellular", "carrier", "storage", "biddable", "startprice", "sprice.predict.diff")
glb_out_obs <- NULL # "all" for bid._sp # select from c(NULL, "all", "new", "trn")
glb_out_vars_lst <- list()
# glb_id_var will be the first output column, by default
### !_sp
glb_out_vars_lst[["Probability1"]] <- "%<d-% paste0(glb_rsp_var_out, glb_fin_mdl_id, '.prob')"
### !_sp
### bid._sp
# glb_out_vars_lst[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glb_out_vars_lst[[paste0(head(unlist(strsplit(glb_rsp_var_out, "")), -1), collapse = "")]] <-
# "%<d-% paste0(glb_rsp_var_out, glb_fin_mdl_id)"
### bid._sp
glb_out_pfx <- "ebayipads_finmdl_RFE_sizes_bid1_"
glb_save_envir <- FALSE # or TRUE
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 8.933 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_to_sav <- function() {
sav_allobs_df <<- glb_allobs_df
sav_trnobs_df <<- glb_trnobs_df
if (any(grepl("glb_fitobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_fitobs_df)) sav_fitobs_df <<- glb_fitobs_df
if (any(grepl("glb_OOBobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_OOBobs_df)) sav_OOBobs_df <<- glb_OOBobs_df
if (any(grepl("glb_newobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_newobs_df)) {
#print("Attempting to save glb_newobs_df...")
sav_newobs_df <<- glb_newobs_df
}
if (any(grepl("glb_ctgry_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_ctgry_df)) sav_ctgry_df <<- glb_ctgry_df
if (!is.null(glb_models_lst )) sav_models_lst <<- glb_models_lst
if (!is.null(glb_models_df )) sav_models_df <<- glb_models_df
if (any(grepl("glb_feats_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_feats_df)) sav_feats_df <<- glb_feats_df
if (any(grepl("glb_featsimp_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_featsimp_df)) sav_featsimp_df <<- glb_featsimp_df
}
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glbFeatsExclude <- union(glbFeatsExclude, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glbFeatsExclude <- union(glbFeatsExclude, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here"); glb_to_sav()
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# - Merge glb_obs_stack_condition & glbObsDropCondition
# - Derive glb_obs_stack|drop_chk_vars from condition automatically
# - Implement glb_obs_stack_condition & glb_obs_stack_chk_vars options
dsp_partition_stats <- function(obs_df, vars=NULL) {
lcl_vars <- NULL
for (var in c(vars, glb_rsp_var_raw)) {
if ((length(unique(obs_df[, var])) > 5) && is.numeric(obs_df[, var])) {
cut_var <- paste0(var, ".cut.fctr")
obs_df[, cut_var] <- cut(obs_df[, var], 3)
lcl_vars <- union(lcl_vars, cut_var)
} else lcl_vars <- union(lcl_vars, var)
}
print("Partition stats:")
print(mycreate_sqlxtab_df(obs_df, union(lcl_vars, ".src")))
for (var in lcl_vars) {
print(freq_df <- mycreate_sqlxtab_df(obs_df, union(var, ".src")))
print(myplot_hbar(freq_df, ".src", ".n", colorcol_name=var))
}
print(mycreate_sqlxtab_df(obs_df, ".src"))
# if (length(unique(glb_allobs_df[, glb_rsp_var_raw])) > 5) {
# cut_var <- paste0(glb_rsp_var_raw, ".cut.fctr")
# glb_allobs_df[, cut_var] <- cut(glb_allobs_df[, glb_rsp_var_raw], 3)
# glbFeatsExclude <- union(glbFeatsExclude, cut_var)
# glb_obs_stack_chk_vars <- union(cut_var, glb_obs_stack_chk_vars)
# } else glb_obs_stack_chk_vars <- union(glb_rsp_var_raw, glb_obs_stack_chk_vars)
# #glb_obs_stack_chk_vars <- union(glb_obs_stack_chk_vars, ".src")
# print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
# print(mycreate_sqlxtab_df(glb_allobs_df, union(glb_obs_stack_chk_vars, ".src")))
# for (var in glb_obs_stack_chk_vars) {
# print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
# }
# print(mycreate_sqlxtab_df(glb_allobs_df, ".src"))
}
myget_symbols <- function(txt) {
if (is.null(txt)) return(NULL)
#print(getParseData(parse(text=txt, keep.source=TRUE)))
return(unique(subset(getParseData(parse(text=txt, keep.source=TRUE)),
token == "SYMBOL")$text))
}
# tokens <- unlist(strsplit(gsub("[[:punct:]|[:space:]]", " ", glbObsDropCondition), " "))
# tokens <- tokens[tokens != ""]
# glb_obs_drop_chk_vars <- c("biddable") # or NULL
dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glbObsDropCondition))## [1] "Partition stats:"## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## UniqueID.cut.fctr productline biddable sold .src .n
## 1 (1e+04,1.09e+04] iPad 2 1 1 Train 90
## 2 (1.18e+04,1.27e+04] iPad 2 0 NA Test 84
## 3 (1e+04,1.09e+04] iPad 1 1 1 Train 78
## 4 (1.18e+04,1.27e+04] iPad 2 1 NA Test 70
## 5 (1.09e+04,1.18e+04] iPad mini 0 0 Train 68
## 6 (1.09e+04,1.18e+04] iPad 2 0 0 Train 67
## 7 (1.09e+04,1.18e+04] Unknown 0 0 Train 64
## 8 (1e+04,1.09e+04] iPad mini 1 1 Train 60
## 9 (1.18e+04,1.27e+04] iPad mini 1 NA Test 59
## 10 (1.09e+04,1.18e+04] iPad Air 2 0 0 Train 54
## 11 (1.18e+04,1.27e+04] Unknown 0 NA Test 54
## 12 (1.09e+04,1.18e+04] iPad Air 0 0 Train 52
## 13 (1.18e+04,1.27e+04] iPad mini 0 NA Test 52
## 14 (1.18e+04,1.27e+04] iPad 1 1 NA Test 50
## 15 (1e+04,1.09e+04] iPad 3 1 1 Train 50
## 16 (1.09e+04,1.18e+04] iPad 1 0 0 Train 43
## 17 (1.18e+04,1.27e+04] iPad 4 0 NA Test 42
## 18 (1e+04,1.09e+04] iPad 2 0 0 Train 42
## 19 (1e+04,1.09e+04] iPad Air 1 1 Train 42
## 20 (1.18e+04,1.27e+04] Unknown 1 NA Test 38
## 21 (1.18e+04,1.27e+04] iPad 1 0 NA Test 38
## 22 (1.18e+04,1.27e+04] iPad Air 0 NA Test 38
## 23 (1.09e+04,1.18e+04] iPad 4 0 0 Train 36
## 24 (1.09e+04,1.18e+04] iPad mini 1 1 Train 36
## 25 (1.18e+04,1.27e+04] iPad Air 1 NA Test 36
## 26 (1.18e+04,1.27e+04] iPad Air 2 0 NA Test 36
## 27 (1.18e+04,1.27e+04] iPad mini 2 1 NA Test 34
## 28 (1e+04,1.09e+04] iPad mini 0 0 Train 34
## 29 (1.09e+04,1.18e+04] iPad mini 3 0 0 Train 33
## 30 (1e+04,1.09e+04] iPad 4 0 0 Train 33
## 31 (1.09e+04,1.18e+04] Unknown 1 1 Train 32
## 32 (1.18e+04,1.27e+04] iPad 3 0 NA Test 32
## 33 (1e+04,1.09e+04] iPad 4 1 1 Train 32
## 34 (1e+04,1.09e+04] iPad Air 0 0 Train 32
## 35 (1.09e+04,1.18e+04] iPad 3 0 0 Train 29
## 36 (1e+04,1.09e+04] iPad 1 0 0 Train 29
## 37 (1e+04,1.09e+04] iPad Air 2 1 1 Train 29
## 38 (1.09e+04,1.18e+04] iPad mini 2 0 0 Train 28
## 39 (1.18e+04,1.27e+04] iPad 4 1 NA Test 26
## 40 (1.18e+04,1.27e+04] iPad Air 2 1 NA Test 26
## 41 (1e+04,1.09e+04] iPad 3 0 0 Train 26
## 42 (1.18e+04,1.27e+04] iPad mini 3 0 NA Test 24
## 43 (1e+04,1.09e+04] Unknown 1 1 Train 24
## 44 (1e+04,1.09e+04] iPad Air 2 0 0 Train 24
## 45 (1e+04,1.09e+04] iPad mini 2 1 1 Train 24
## 46 (1.09e+04,1.18e+04] iPad 2 1 1 Train 23
## 47 (1.09e+04,1.18e+04] iPad mini 1 0 Train 23
## 48 (1.18e+04,1.27e+04] iPad 3 1 NA Test 23
## 49 (1e+04,1.09e+04] Unknown 0 0 Train 23
## 50 (1.18e+04,1.27e+04] iPad mini 2 0 NA Test 22
## 51 (1.09e+04,1.18e+04] iPad Air 2 1 1 Train 21
## 52 (1.09e+04,1.18e+04] Unknown 1 0 Train 20
## 53 (1.09e+04,1.18e+04] iPad Air 1 1 Train 19
## 54 (1.09e+04,1.18e+04] iPad mini 0 1 Train 18
## 55 (1.09e+04,1.18e+04] iPad 2 0 1 Train 17
## 56 (1e+04,1.09e+04] iPad mini 2 0 0 Train 16
## 57 (1.09e+04,1.18e+04] Unknown 0 1 Train 15
## 58 (1.09e+04,1.18e+04] iPad 1 1 1 Train 15
## 59 (1.09e+04,1.18e+04] iPad 2 1 0 Train 15
## 60 (1e+04,1.09e+04] iPad 1 1 0 Train 15
## 61 (1.18e+04,1.27e+04] iPad mini 3 1 NA Test 14
## 62 (1e+04,1.09e+04] iPad 2 0 1 Train 14
## 63 (1e+04,1.09e+04] iPad mini 0 1 Train 14
## 64 (1e+04,1.09e+04] iPad mini 1 0 Train 14
## 65 (1e+04,1.09e+04] iPad mini 3 0 0 Train 14
## 66 (1.09e+04,1.18e+04] iPad 1 0 1 Train 13
## 67 (1e+04,1.09e+04] iPad 1 0 1 Train 13
## 68 (1.09e+04,1.18e+04] iPad 3 1 1 Train 12
## 69 (1.09e+04,1.18e+04] iPad Air 2 0 1 Train 12
## 70 (1e+04,1.09e+04] iPad mini 3 1 1 Train 12
## 71 (1.09e+04,1.18e+04] iPad 4 1 1 Train 11
## 72 (1.09e+04,1.18e+04] iPad Air 2 1 0 Train 11
## 73 (1e+04,1.09e+04] iPad 3 0 1 Train 11
## 74 (1.09e+04,1.18e+04] iPad mini 2 0 1 Train 10
## 75 (1.09e+04,1.18e+04] iPad mini 2 1 1 Train 10
## 76 (1e+04,1.09e+04] iPad 2 1 0 Train 10
## 77 (1e+04,1.09e+04] iPad 4 1 0 Train 10
## 78 (1.09e+04,1.18e+04] iPad 4 1 0 Train 9
## 79 (1e+04,1.09e+04] iPad 3 1 0 Train 9
## 80 (1e+04,1.09e+04] iPad Air 0 1 Train 9
## 81 (1.09e+04,1.18e+04] iPad 4 0 1 Train 8
## 82 (1.09e+04,1.18e+04] iPad mini 2 1 0 Train 8
## 83 (1.09e+04,1.18e+04] iPad mini 3 1 0 Train 8
## 84 (1.18e+04,1.27e+04] iPad 1 0 0 Train 8
## 85 (1e+04,1.09e+04] iPad 4 0 1 Train 8
## 86 (1.09e+04,1.18e+04] iPad 1 1 0 Train 7
## 87 (1.18e+04,1.27e+04] Unknown 0 0 Train 7
## 88 (1.18e+04,1.27e+04] iPad Air 2 0 0 Train 7
## 89 (1e+04,1.09e+04] iPad Air 1 0 Train 7
## 90 (1e+04,1.09e+04] iPad Air 2 0 1 Train 7
## 91 (1.09e+04,1.18e+04] iPad Air 0 1 Train 6
## 92 (1.09e+04,1.18e+04] iPad Air 1 0 Train 6
## 93 (1.09e+04,1.18e+04] iPad mini 3 0 1 Train 6
## 94 (1.09e+04,1.18e+04] iPad mini 3 1 1 Train 6
## 95 (1.18e+04,1.27e+04] Unknown 0 1 Train 6
## 96 (1.18e+04,1.27e+04] iPad mini 0 0 Train 6
## 97 (1e+04,1.09e+04] Unknown 1 0 Train 6
## 98 (1.09e+04,1.18e+04] iPad 3 0 1 Train 5
## 99 (1.09e+04,1.18e+04] iPad 3 1 0 Train 5
## 100 (1.18e+04,1.27e+04] iPad 2 0 0 Train 5
## 101 (1.18e+04,1.27e+04] iPad 4 0 0 Train 5
## 102 (1.18e+04,1.27e+04] iPad Air 0 0 Train 5
## 103 (1e+04,1.09e+04] Unknown 0 1 Train 5
## 104 (1.18e+04,1.27e+04] iPad 3 0 0 Train 4
## 105 (1.18e+04,1.27e+04] iPad mini 3 0 0 Train 4
## 106 (1e+04,1.09e+04] iPad Air 2 1 0 Train 4
## 107 (1e+04,1.09e+04] iPad mini 2 0 1 Train 4
## 108 (1e+04,1.09e+04] iPad mini 2 1 0 Train 4
## 109 (1.09e+04,1.18e+04] iPad mini Retina 0 0 Train 3
## 110 (1.18e+04,1.27e+04] iPad 1 0 1 Train 3
## 111 (1.18e+04,1.27e+04] iPad 1 1 1 Train 3
## 112 (1.18e+04,1.27e+04] iPad 4 0 1 Train 3
## 113 (1.18e+04,1.27e+04] iPad mini 1 1 Train 3
## 114 (1e+04,1.09e+04] iPad mini 3 0 1 Train 3
## 115 (1e+04,1.09e+04] iPad mini 3 1 0 Train 3
## 116 (1.18e+04,1.27e+04] Unknown 1 0 Train 2
## 117 (1.18e+04,1.27e+04] iPad 2 1 1 Train 2
## 118 (1.18e+04,1.27e+04] iPad 4 1 1 Train 2
## 119 (1.18e+04,1.27e+04] iPad Air 0 1 Train 2
## 120 (1.18e+04,1.27e+04] iPad mini 2 0 0 Train 2
## 121 (1.09e+04,1.18e+04] iPad 5 1 1 Train 1
## 122 (1.09e+04,1.18e+04] iPad mini Retina 0 1 Train 1
## 123 (1.18e+04,1.27e+04] iPad 2 0 1 Train 1
## 124 (1.18e+04,1.27e+04] iPad 3 0 1 Train 1
## 125 (1.18e+04,1.27e+04] iPad 3 1 1 Train 1
## 126 (1.18e+04,1.27e+04] iPad Air 2 0 1 Train 1
## 127 (1.18e+04,1.27e+04] iPad Air 2 1 1 Train 1
## 128 (1.18e+04,1.27e+04] iPad mini 0 1 Train 1
## 129 (1.18e+04,1.27e+04] iPad mini 2 0 1 Train 1
## 130 (1.18e+04,1.27e+04] iPad mini 3 1 0 Train 1
## 131 (1.18e+04,1.27e+04] iPad mini Retina 0 1 Train 1
## 132 (1e+04,1.09e+04] iPad mini Retina 0 0 Train 1
## 133 (1e+04,1.09e+04] iPad mini Retina 0 1 Train 1
## 134 (1e+04,1.09e+04] iPad mini Retina 1 1 Train 1
## UniqueID.cut.fctr .src .n
## 1 (1e+04,1.09e+04] Train 887
## 2 (1.09e+04,1.18e+04] Train 886
## 3 (1.18e+04,1.27e+04] Test 798
## 4 (1.18e+04,1.27e+04] Train 88
## productline .src .n
## 1 iPad 2 Train 286
## 2 iPad mini Train 277
## 3 iPad 1 Train 227
## 4 Unknown Train 204
## 5 iPad Air Train 180
## 6 iPad Air 2 Train 171
## 7 iPad 4 Train 157
## 8 iPad 2 Test 154
## 9 iPad 3 Train 153
## 10 iPad mini Test 111
## 11 iPad mini 2 Train 107
## 12 Unknown Test 92
## 13 iPad mini 3 Train 90
## 14 iPad 1 Test 88
## 15 iPad Air Test 74
## 16 iPad 4 Test 68
## 17 iPad Air 2 Test 62
## 18 iPad mini 2 Test 56
## 19 iPad 3 Test 55
## 20 iPad mini 3 Test 38
## 21 iPad mini Retina Train 8
## 22 iPad 5 Train 1
## biddable .src .n
## 1 0 Train 1024
## 2 1 Train 837
## 3 0 Test 422
## 4 1 Test 376
## sold .src .n
## 1 0 Train 1001
## 2 1 Train 860
## 3 NA Test 798
## .src .n
## 1 Train 1861
## 2 Test 798if (!is.null(glbObsDropCondition)) {
print(sprintf("Running glbObsDropCondition filter: %s", glbObsDropCondition))
glb_allobs_df <- do.call("subset",
list(glb_allobs_df, parse(text=paste0("!(", glbObsDropCondition, ")"))))
dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glbObsDropCondition))
}## [1] "Running glbObsDropCondition filter: (UniqueID %in% c(NULL\n , 11234 #sold=0; 2 other dups(10306, 11503) are sold=1\n , 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1\n )) | \n (productline %in% c('iPad 5', 'iPad mini Retina')) |\n # (biddable != 0) # bid0_sp\n (biddable == 0) # bid1_sp\n "
## [1] "Partition stats:"
## UniqueID.cut.fctr productline biddable sold .src .n
## 1 (1e+04,1.09e+04] iPad 2 1 1 Train 90
## 2 (1e+04,1.09e+04] iPad 1 1 1 Train 78
## 3 (1.18e+04,1.27e+04] iPad 2 1 NA Test 70
## 4 (1e+04,1.09e+04] iPad mini 1 1 Train 60
## 5 (1.18e+04,1.27e+04] iPad mini 1 NA Test 59
## 6 (1.18e+04,1.27e+04] iPad 1 1 NA Test 50
## 7 (1e+04,1.09e+04] iPad 3 1 1 Train 50
## 8 (1e+04,1.09e+04] iPad Air 1 1 Train 42
## 9 (1.18e+04,1.27e+04] Unknown 1 NA Test 38
## 10 (1.18e+04,1.27e+04] iPad Air 1 NA Test 36
## 11 (1.09e+04,1.18e+04] iPad mini 1 1 Train 35
## 12 (1.18e+04,1.27e+04] iPad mini 2 1 NA Test 34
## 13 (1.09e+04,1.18e+04] Unknown 1 1 Train 32
## 14 (1e+04,1.09e+04] iPad 4 1 1 Train 32
## 15 (1e+04,1.09e+04] iPad Air 2 1 1 Train 29
## 16 (1.18e+04,1.27e+04] iPad 4 1 NA Test 26
## 17 (1.18e+04,1.27e+04] iPad Air 2 1 NA Test 26
## 18 (1e+04,1.09e+04] Unknown 1 1 Train 24
## 19 (1e+04,1.09e+04] iPad mini 2 1 1 Train 24
## 20 (1.09e+04,1.18e+04] iPad 2 1 1 Train 23
## 21 (1.09e+04,1.18e+04] iPad mini 1 0 Train 23
## 22 (1.18e+04,1.27e+04] iPad 3 1 NA Test 23
## 23 (1.09e+04,1.18e+04] iPad Air 2 1 1 Train 21
## 24 (1.09e+04,1.18e+04] Unknown 1 0 Train 20
## 25 (1.09e+04,1.18e+04] iPad Air 1 1 Train 19
## 26 (1.09e+04,1.18e+04] iPad 1 1 1 Train 15
## 27 (1.09e+04,1.18e+04] iPad 2 1 0 Train 15
## 28 (1e+04,1.09e+04] iPad 1 1 0 Train 15
## 29 (1.18e+04,1.27e+04] iPad mini 3 1 NA Test 14
## 30 (1e+04,1.09e+04] iPad mini 1 0 Train 14
## 31 (1.09e+04,1.18e+04] iPad 3 1 1 Train 12
## 32 (1e+04,1.09e+04] iPad mini 3 1 1 Train 12
## 33 (1.09e+04,1.18e+04] iPad 4 1 1 Train 11
## 34 (1.09e+04,1.18e+04] iPad Air 2 1 0 Train 11
## 35 (1.09e+04,1.18e+04] iPad mini 2 1 1 Train 10
## 36 (1e+04,1.09e+04] iPad 2 1 0 Train 10
## 37 (1e+04,1.09e+04] iPad 4 1 0 Train 10
## 38 (1.09e+04,1.18e+04] iPad 4 1 0 Train 9
## 39 (1e+04,1.09e+04] iPad 3 1 0 Train 9
## 40 (1.09e+04,1.18e+04] iPad mini 2 1 0 Train 8
## 41 (1.09e+04,1.18e+04] iPad mini 3 1 0 Train 8
## 42 (1.09e+04,1.18e+04] iPad 1 1 0 Train 7
## 43 (1e+04,1.09e+04] iPad Air 1 0 Train 7
## 44 (1.09e+04,1.18e+04] iPad Air 1 0 Train 6
## 45 (1.09e+04,1.18e+04] iPad mini 3 1 1 Train 6
## 46 (1e+04,1.09e+04] Unknown 1 0 Train 6
## 47 (1.09e+04,1.18e+04] iPad 3 1 0 Train 5
## 48 (1.18e+04,1.27e+04] iPad mini 1 1 Train 4
## 49 (1e+04,1.09e+04] iPad Air 2 1 0 Train 4
## 50 (1e+04,1.09e+04] iPad mini 2 1 0 Train 4
## 51 (1.18e+04,1.27e+04] iPad 1 1 1 Train 3
## 52 (1e+04,1.09e+04] iPad mini 3 1 0 Train 3
## 53 (1.18e+04,1.27e+04] Unknown 1 0 Train 2
## 54 (1.18e+04,1.27e+04] iPad 2 1 1 Train 2
## 55 (1.18e+04,1.27e+04] iPad 4 1 1 Train 2
## 56 (1.18e+04,1.27e+04] iPad 3 1 1 Train 1
## 57 (1.18e+04,1.27e+04] iPad Air 2 1 1 Train 1
## 58 (1.18e+04,1.27e+04] iPad mini 3 1 0 Train 1
## UniqueID.cut.fctr .src .n
## 1 (1e+04,1.09e+04] Train 523
## 2 (1.18e+04,1.27e+04] Test 376
## 3 (1.09e+04,1.18e+04] Train 296
## 4 (1.18e+04,1.27e+04] Train 16
## productline .src .n
## 1 iPad 2 Train 140
## 2 iPad mini Train 136
## 3 iPad 1 Train 118
## 4 Unknown Train 84
## 5 iPad 3 Train 77
## 6 iPad Air Train 74
## 7 iPad 2 Test 70
## 8 iPad Air 2 Train 66
## 9 iPad 4 Train 64
## 10 iPad mini Test 59
## 11 iPad 1 Test 50
## 12 iPad mini 2 Train 46
## 13 Unknown Test 38
## 14 iPad Air Test 36
## 15 iPad mini 2 Test 34
## 16 iPad mini 3 Train 30
## 17 iPad 4 Test 26
## 18 iPad Air 2 Test 26
## 19 iPad 3 Test 23
## 20 iPad mini 3 Test 14
## biddable .src .n
## 1 1 Train 835
## 2 1 Test 376
## sold .src .n
## 1 1 Train 638
## 2 NA Test 376
## 3 0 Train 197
## .src .n
## 1 Train 835
## 2 Test 376# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 165 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 1351 1 150.00 Used Unknown
## 1386 1 150.00 Used Unknown
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 1351 Unknown Unknown Unknown Unknown 1 11351 Train
## 1386 Unknown Unknown Unknown Unknown 0 11386 Train
## description
## 233
## 487
## 1646 PLEASE READ THE ENTIRE DESCRIPTION! THE ANSWER TO YOUR QUESTION(S) ARE USUALLY ALREADY IN THE
## 442
## 2214
## 1072 Definite must-have. This Apple iPad Mini is pre-owned and has some signs of use, there is a nick/
## biddable startprice condition cellular carrier color
## 233 1 0.99 Used 0 None Unknown
## 487 1 80.00 Used 0 None Black
## 1646 1 40.00 For parts or not working 0 None Unknown
## 442 1 9.99 Used 0 None White
## 2214 1 150.00 Used 0 None Unknown
## 1072 1 199.00 Used 1 AT&T Unknown
## storage productline sold UniqueID .src
## 233 16 iPad 2 1 10233 Train
## 487 16 iPad 2 1 10487 Train
## 1646 64 iPad 2 1 11646 Train
## 442 16 iPad 3 1 10442 Train
## 2214 16 iPad mini NA 12214 Test
## 1072 64 iPad mini 0 11072 Train
## description
## 1223 Good condition and functional 32GB iPad Mini Wifi - Black. Please read full discription
## 2388 Good condition and functional 32GB iPad Mini Wifi - Black. Please read full discription
## 524
## 2111
## 510
## 2126
## biddable startprice condition cellular carrier color storage
## 1223 1 179 Used 0 None Black 32
## 2388 1 179 Used 0 None Black 32
## 524 1 200 Used 0 None Space Gray 64
## 2111 1 200 Used 0 None Space Gray 64
## 510 1 99 Used 0 None Space Gray 16
## 2126 1 99 Used 0 None Space Gray 16
## productline sold UniqueID .src
## 1223 iPad mini 0 11223 Train
## 2388 iPad mini NA 12388 Test
## 524 iPad mini 2 1 10524 Train
## 2111 iPad mini 2 NA 12111 Test
## 510 iPad mini 3 1 10510 Train
## 2126 iPad mini 3 NA 12126 Test# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 23 23 0 6 3 9
## 56 56 0 4 1 5
## 4 4 0 1 3 4
## 10 10 0 3 1 4
## 20 20 0 3 1 4
## 11 11 0 2 1 3
## .grpid sold.0 sold.1 sold.NA .freq
## 49 49 0 2 1 3
## 16 16 0 0 2 2
## 25 25 0 1 1 2
## 31 31 0 0 2 2
## 44 44 0 2 0 2
## 48 48 2 0 0 2
## .grpid sold.0 sold.1 sold.NA .freq
## 63 63 0 1 1 2
## 65 65 0 1 1 2
## 66 66 2 0 0 2
## 67 67 1 0 1 2
## 68 68 0 1 1 2
## 69 69 0 1 1 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 3 3 1 1 0 2
## 59 59 1 1 0 2
## 60 60 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
#subset(glb_allobs_df, .grpid %in% c(25))
#mydsp_obs(list(productline.contains="iPad 1", storage.contains="16", color.contains="Black", carrier.contains="None", cellular.contains="0", condition.contains="Used", startprice=80), cols=c("productline", "storage", "color", "carrier", "cellular", "condition", "startprice", "sold"))
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 4 4 0 1 3 4
## 7 7 0 1 1 2
## 8 8 0 0 2 2
## 10 10 0 3 1 4
## 11 11 0 2 1 3
## 12 12 0 2 1 3
## 15 15 0 2 1 3
## 16 16 0 0 2 2
## 17 17 0 1 1 2
## 19 19 0 2 1 3
## 20 20 0 3 1 4
## 21 21 0 1 1 2
## 23 23 0 6 3 9
## 24 24 0 0 2 2
## 25 25 0 1 1 2
## 26 26 0 1 1 2
## 27 27 0 2 1 3
## 30 30 0 1 1 2
## 31 31 0 0 2 2
## 32 32 0 1 2 3
## 34 34 0 1 1 2
## 36 36 1 0 2 3
## 37 37 1 0 1 2
## 38 38 1 0 1 2
## 41 41 0 1 1 2
## 47 47 0 1 1 2
## 49 49 0 2 1 3
## 52 52 0 1 1 2
## 54 54 0 1 1 2
## 56 56 0 4 1 5
## 57 57 0 1 1 2
## 58 58 0 1 1 2
## 61 61 1 0 1 2
## 63 63 0 1 1 2
## 64 64 0 2 1 3
## 65 65 0 1 1 2
## 67 67 1 0 1 2
## 68 68 0 1 1 2
## 69 69 0 1 1 2glbFeatsExclude <- c(".grpid", glbFeatsExclude)
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
if (!is.null(glbInpMerge)) {
print("Running glbInpMerge specs...")
obsMrg <- data.frame()
for (fName in glbInpMerge$fnames) {
print(sprintf(" Appending rows from %s...", fName))
obsMrg <- rbind(obsMrg, read.csv(fName))
}
glb_allobs_df <- merge(glb_allobs_df, obsMrg, all.x = TRUE)
}## [1] "Running glbInpMerge specs..."
## [1] " Appending rows from ebayipads_finmdl_bid0_sp_out.csv..."
## [1] " Appending rows from ebayipads_mdlens_bid1_sp_out.csv..."dsp_partition_stats(obs_df = glb_allobs_df,
vars = myget_symbols(glb_obs_repartition_train_condition))## [1] "Partition stats:"
## sold .src .n
## 1 1 Train 638
## 2 NA Test 376
## 3 0 Train 197
## sold .src .n
## 1 1 Train 638
## 2 NA Test 376
## 3 0 Train 197
## .src .n
## 1 Train 835
## 2 Test 376if (!is.null(glb_obs_repartition_train_condition)) {
print(sprintf("Running glb_obs_repartition_train_condition filter: %s",
glb_obs_repartition_train_condition))
# glb_allobs_df <- mutate(glb_allobs_df, .src=ifelse(!is.na(sold) & (sold == 1),
# "Train", "Test"))
# glb_allobs_df <- mutate_(glb_allobs_df,
# .src=interp(ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")))
# glb_allobs_df <- within(glb_allobs_df, {
# .src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")
# })
# glb_allobs_df <- within(glb_allobs_df, {
# if(eval(parse(text="!is.na(sold) & (sold == 1)"))) .src <- "Train" else
# .src <- "Test"
# })
# with(glb_allobs_df, {
# src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")
# })
# glb_allobs_df$.src <- sapply(1:nrow(glb_allobs_df), function (row_ix) ifelse)
# glb_allobs_df[parse(text=paste0("!(", glbObsDropCondition, ")")), ".src"] <- do.call("subset",
# list(glb_allobs_df, ))
glb_trnobs_df <- do.call("subset", list(glb_allobs_df,
parse(text=paste0(" (", glb_obs_repartition_train_condition, ")"))))
glb_trnobs_df$.src <- "Train"
glb_newobs_df <- do.call("subset", list(glb_allobs_df,
parse(text=paste0("!(", glb_obs_repartition_train_condition, ")"))))
glb_newobs_df$.src <- "Test"
glb_allobs_df <- rbind(glb_trnobs_df, glb_newobs_df)
dsp_partition_stats(obs_df = glb_allobs_df,
vars = myget_symbols(glb_obs_repartition_train_condition))
}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 8.933 18.808 9.875
## 2 inspect.data 2 0 0 18.809 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function(featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals) {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df, featsExclude, fctrMaxUniqVals)
}
glb_chk_data()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 376 rows containing non-finite values (stat_bin).## Loading required package: reshape2
## sold.0 sold.1 sold.NA
## Test NA NA 376
## Train 197 638 NA
## sold.0 sold.1 sold.NA
## Test NA NA 1
## Train 0.2359281 0.7640719 NA
## [1] "numeric data missing in : "
## sold
## 376
## [1] "numeric data w/ 0s in : "
## sold
## 197
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## sold sold.fctr .n
## 1 1 Y 638
## 2 NA <NA> 376
## 3 0 N 197## Warning: Removed 1 rows containing missing values (position_stack).
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 376
## Train 197 638 NA
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 1
## Train 0.2359281 0.7640719 NA# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(feats_vctr) / 2.0), 2)))
pltIx <- 1
for (feat in feats_vctr) {
#print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
#gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
gp <- myplot_violin(glb_allobs_df, ycol_names = feat, xcol_name = glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp + labs(title = feat),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
}
# dsp_numeric_feats_dstrb(setdiff(names(glb_allobs_df), union(myfind_chr_cols_df(glb_allobs_df), c(glb_rsp_var_raw, glb_rsp_var)))[2:6]) # dsp_numeric_feats_dstrb(c("startprice", "sprice.root3", "sprice.predict.diff"))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
set.seed(169)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glbFeatsExclude)))
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 18.809 23.653 4.844
## 3 scrub.data 2 1 1 23.654 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals)## [1] "numeric data missing in : "
## sold sold.fctr
## 376 376
## [1] "numeric data w/ 0s in : "
## sold
## 197
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 788 0 0 0 0 0
## 1 116 0 3 11 11 74 76
## Unknown 1 1 2 0 0 128 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 789 0 0 0 0 0
## 1 117 0 5 11 11 74 76
## Unknown 0 0 0 0 0 128 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 23.654 24.715 1.061
## 4 transform.data 2 2 2 24.716 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glbFeatsExclude <- union(glbFeatsExclude, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glbFeatsExclude <- union(glbFeatsExclude, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glbFeatsDerive[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glbFeatsDerive[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: sprice.root2..."
## [1] "Creating new feature: sprice.log10..."
## [1] "Creating new feature: sprice.d20nexp..."
## [1] "Creating new feature: sprice.predict.diff..."
## [1] "Creating new feature: spdiff.cut.fctr..."
## [1] "Creating new feature: descr.my..."
## [1] "Creating new feature: prdl.descr.my.fctr..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10029, 10948, 10136, 10178, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
# glb_allobs_df[row_pos, "descr.my"] <-
# gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glbFeatsText)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 24.716 25.024 0.308
## 5 extract.features 3 0 0 25.025 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor label_minor bgn end
## 1 extract.features_bgn 1 0 0 25.032 NA
## elapsed
## 1 NA# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glbFeatsExclude <-
union(glbFeatsExclude,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glbFeatsExclude <- union(glbFeatsExclude,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor label_minor
## 1 extract.features_bgn 1 0 0
## 2 extract.features_factorize.str.vars 2 0 0
## bgn end elapsed
## 1 25.032 25.047 0.015
## 2 25.047 NA NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid descr.my
## "storage" "productline" ".src" ".grpid" "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glbFeatsExclude, glbFeatsText))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glbFeatsExclude <- union(glbFeatsExclude, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5if (!is.null(glbFeatsText)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
#print(txt_vctr <- glb_allobs_df[glb_allobs_df$UniqueID == 11329, "descr.my"])
#strsplit(txt_vctr, "")[[1]][1]
#ptn_ix <- 2; glb_txt_map_df[ptn_ix, ]
#gsub(glb_txt_map_df[ptn_ix, "rex_str"], glb_txt_map_df[ptn_ix, "rpl_str"], txt_vctr)
#print(match_lst <- gregexpr(glb_txt_map_df[ptn_ix, "rex_str"], txt_vctr))
#strsplit(glb_txt_map_df[ptn_ix, "rex_str"], "")[[1]]
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_chr_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_chr_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_chr_lst; all.equal(sav_txt_lst, glb_txt_chr_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_chr_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_chr_lst <- list();
print(sprintf("Building glb_txt_chr_lst..."))
glb_txt_chr_lst <- foreach(txt_var = glbFeatsText) %dopar% {
# for (txt_var in glbFeatsText) {
txt_vctr <- glb_allobs_df[, txt_var]
names(txt_vctr) <- glb_allobs_df[, glb_id_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glbFeatsText[3]; txt_vctr <- glb_txt_chr_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_chr_lst) <- glbFeatsText
for (txt_var in glbFeatsText) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_chr_lst[[glbFeatsText[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_chr_lst
for (txt_var in glbFeatsText) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glbFeatsText) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_chr_lst
for (txt_var in glbFeatsText) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glbFeatsText) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
# Enhancements:
# - arg should be txt_corpus instead of txt_vctr
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy = TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy = TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes = TRUE, lazy = FALSE)
# Defaulting to Tf since TfIdf with normalize = TRUE throws a warning for empty docs
terms_mtrx <- as.matrix(TermDocumentMatrix(txt_corpus,
control = list(weighting = weightTf)))
terms_df <- orderBy(~ -Tf, data.frame(term = dimnames(terms_mtrx)$Terms,
Tf = rowSums(terms_mtrx)))
cmpnd_df <- subset(terms_df, grepl("-", term))
if (nrow(cmpnd_df) == 0) {
print(" No compounded terms found")
return(FALSE)
}
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_df[!cmpnd_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_df[!cmpnd_df$filter, "term"], ignore.case=TRUE)
cmpnd_df <- subset(cmpnd_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_df): %d", nrow(cmpnd_df)))
myprint_df(cmpnd_df)
}
# This should be run after glb_txt_corpus_lst is created with tolower
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
# for (txt_var in glbFeatsText) {
# print(sprintf("Remaining compound terms in %s: ", txt_var))
# find_cmpnd_wrds(txt_vctr = glb_txt_chr_lst[[txt_var]])
# #grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
# #rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
# }
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_txt_terms <- function(terms_TDM) {
terms_mtrx <- as.matrix(as.TermDocumentMatrix(terms_TDM))
docms_mtrx <- as.matrix(as.DocumentTermMatrix(terms_TDM))
terms_df <- data.frame(term = dimnames(terms_mtrx)$Terms,
weight = rowSums(terms_mtrx),
freq = rowSums(terms_mtrx > 0))
terms_df$pos <- 1:nrow(terms_df)
terms_df$cor.y <-
cor(docms_mtrx[glb_allobs_df$.src == "Train",],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs")
terms_df$cor.y.abs <- abs(terms_df$cor.y)
# .rnorm.cor.y.abs <- abs(cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
# as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
# use = "pairwise.complete.obs"))
terms_df$chisq.stat <- NA; terms_df$chisq.pval <- NA
for (ix in 1:nrow(terms_df)) {
#for (ix in 1:743) {
if (length(unique(docms_mtrx[glb_allobs_df$.src == "Train", ix])) > 1) {
chisq <- chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var])
terms_df[ix, "chisq.stat"] <- chisq$statistic
terms_df[ix, "chisq.pval"] <- chisq$p.value
}
}
nzv_df <- nzv(docms_mtrx[glb_allobs_df$.src == "Train",], freqCut = glb_nzv_freqCut,
uniqueCut = glb_nzv_uniqueCut, saveMetrics = TRUE)
terms_df$nzv.freqRatio <- nzv_df$freqRatio
# terms_df$nzv.freqRatio.cut.fctr <- cut(terms_df$nzv.freqRatio,
# breaks = sort(c(min(terms_df$nzv.freqRatio),
# glb_nzv_freqCut,
# max(terms_df$nzv.freqRatio))))
terms_df$nzv.percentUnique <- nzv_df$percentUnique
# terms_df$nzv.percentUnique.cut.fctr <- cut(terms_df$nzv.percentUnique,
# breaks = sort(c(min(terms_df$nzv.percentUnique) - .Machine$double.neg.eps,
# glb_nzv_uniqueCut,
# max(terms_df$nzv.percentUnique))))
# terms_df$nzv.quad.fctr <- as.factor(paste0("fRatio:", terms_df$nzv.freqRatio.cut.fctr,
# "\n%Unq:", terms_df$nzv.percentUnique.cut.fctr))
terms_df$nzv <- nzv_df$nzv
for (cls in unique(glb_allobs_df[, glb_txt_cor_var])) {
if (!is.na(cls))
terms_df[, paste0("weight.", as.character(cls))] <-
colSums(t(terms_mtrx) *
as.numeric(!is.na(glb_allobs_df[, glb_txt_cor_var]) &
(glb_allobs_df[, glb_txt_cor_var] == cls))) else
terms_df[, paste0("weight.", as.character(cls))] <-
colSums(t(terms_mtrx) *
as.numeric(is.na(glb_allobs_df[, glb_txt_cor_var])))
}
# Check all calls to get_terms_DTM_terms to change returned order assumption
return(terms_df <- orderBy(~ -weight, terms_df))
}
#plt_full_df <- get_terms_DTM_terms(terms_DTM=glb_full_terms_DTM_lst[[txt_var]])
get_corpus_terms <- function(txt_corpus) {
return(terms_df <- get_txt_terms(terms_TDM =
TermDocumentMatrix(txt_corpus, control = glb_txt_terms_control)))
}
myreplacePunctuation <- function(x) {
return(gsub("[[:punct:]]+", " ", gsub("('|-)", "", x)))
}
#stop(here"); glb_to_sav()
glb_txt_corpus_lst <- list()
print(sprintf("Building glb_txt_corpus_lst..."))
glb_txt_corpus_lst <- foreach(txt_var = glbFeatsText, .verbose = FALSE) %dopar% {
#glb_txt_corpus_lst <- foreach(txt_var = glbFeatsText, .verbose = TRUE) %do% {
#for (txt_var in glbFeatsText) {
txt_corpus <- Corpus(VectorSource(glb_txt_chr_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy = TRUE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy = TRUE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
#txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy = TRUE) #npnct<chr_ix>
txt_corpus <- tm_map(txt_corpus, content_transformer(myreplacePunctuation)
, lazy = TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
if (!is.null(glb_txt_stop_words[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, removeWords, glb_txt_stop_words[[txt_var]],
lazy = FALSE)#, lazy=TRUE) #nstopwrds
# foreach result is based on .Last.Eval
txt_corpus <- txt_corpus
# glb_txt_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_txt_corpus_lst) <- glbFeatsText
mycombineSynonyms <- content_transformer(function(x, syn=NULL) {
Reduce(function(a,b) {
gsub(paste0("\\b(", paste(b$syns, collapse = "|"),")\\b"), b$word, a)}, syn, x)
})
#stop(here"); glb_to_sav(); sav_txt_corpus <- glb_txt_corpus_lst[[txt_var]]; all.equal(sav_txt_corpus, glb_txt_corpus_lst[[txt_var]]); glb_txt_corpus_lst[[txt_var]] <- sav_txt_corpus
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_terms_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_terms_mtrx_lst <- list();
for (txt_var in glbFeatsText) {
print(sprintf(" Top_n post-stop term weights for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_terms_df <-
get_corpus_terms(txt_corpus=glb_txt_corpus_lst[[txt_var]]),
glbFeatsTextTermsMax[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_terms_df
terms_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]],
control=glb_txt_terms_control))
rownames(terms_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_terms_mtrx_lst[[txt_var]] <- terms_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.post.stop.n <- rowSums(terms_stop_mtrx > 0)
tmp_allobs_df$terms.post.stop.n.log <- log(1 + tmp_allobs_df$terms.post.stop.n)
tmp_allobs_df$weight.post.stop.sum <- rowSums(terms_stop_mtrx)
print(sprintf(" Top_n stem term weights for %s:", txt_var))
glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]], stemDocument,
"english", lazy=FALSE)
if (!is.null(glb_txt_synonyms[[txt_var]])) {
syn_lst <- myrmNullObj(glb_txt_synonyms[[txt_var]])
glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]],
mycombineSynonyms,
syn_lst, lazy=FALSE)
}
print(myprint_df(full_terms_df <- get_corpus_terms(glb_txt_corpus_lst[[txt_var]]),
glbFeatsTextTermsMax[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_terms_df
terms_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]],
control=glb_txt_terms_control))
rownames(terms_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_terms_mtrx_lst[[txt_var]] <- terms_stem_mtrx
tmp_allobs_df$terms.post.stem.n <- rowSums(terms_stem_mtrx > 0)
tmp_allobs_df$terms.post.stem.n.log <- log(1 + tmp_allobs_df$terms.post.stem.n)
tmp_allobs_df$weight.post.stem.sum <- rowSums(terms_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.post.stem.n / tmp_allobs_df$terms.post.stop.n
tmp_allobs_df[(is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio) |
is.infinite(tmp_allobs_df$terms.n.stem.stop.Ratio)),
"terms.n.stem.stop.Ratio"] <- 1.0
if ((n.errors <- sum(tmp_allobs_df$terms.n.stem.stop.Ratio > 1)) > 0)
stop(n.errors, " obs in tmp_allobs_df have terms.n.stem.stop.Ratio > 1",
" happening due to terms filtered by glb_txt_terms_control$bounds$global[1] but stemmable to other terms")
#print(head(subset(tmp_allobs_df, terms.n.stem.stop.Ratio > 1)))
#glb_allobs_df[(row_ix <- which(glb_allobs_df$UniqueID == 10465)), ]
#terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]
#setdiff(names(terms_stem_mtrx[row_ix, terms_stem_mtrx[row_ix, ] > 0]), names(terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]))
#mydsp_obs(list(descr.my.contains="updat"))
tmp_allobs_df$weight.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$weight.post.stem.sum / tmp_allobs_df$weight.post.stop.sum
tmp_allobs_df[is.nan(tmp_allobs_df$weight.sum.stem.stop.Ratio) |
is.infinite(tmp_allobs_df$weight.sum.stem.stop.Ratio),
"weight.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df), sep = "")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glbFeatsExclude <- c(glbFeatsExclude,
paste(paste0(txt_var_pfx, ".terms.post."), c("stop.n", "stem.n"), sep = ""))
}
require(wordcloud)
for (txt_var in glbFeatsText) {
print(sprintf(" Wordcloud post-stem term weights for %s:", txt_var))
m <- glb_post_stem_words_terms_mtrx_lst[[txt_var]]
# calculate the frequency of words
v <- sort(colSums(m), decreasing = TRUE)
myNames <- names(v)
d <- data.frame(word = myNames, freq = v)
wordcloud(d$word, d$freq, min.freq = glb_txt_terms_control$bounds$global[1])
}
for (txt_var in glbFeatsText) {
.rnorm.cor.y.abs <- abs(cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs"))
plt_df <- subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))
plt_df$nzv.freqRatio.cut.fctr <- cut(plt_df$nzv.freqRatio,
breaks = sort(c(min(plt_df$nzv.freqRatio),
glb_nzv_freqCut,
max(plt_df$nzv.freqRatio))))
plt_df$nzv.percentUnique.cut.fctr <- cut(plt_df$nzv.percentUnique,
breaks = sort(c(min(plt_df$nzv.percentUnique) - .Machine$double.neg.eps,
glb_nzv_uniqueCut,
max(plt_df$nzv.percentUnique))))
plt_df$nzv.quad.fctr <- as.factor(paste0("fRatio:", plt_df$nzv.freqRatio.cut.fctr,
"\n%Unq:", plt_df$nzv.percentUnique.cut.fctr))
labelCnd <- !plt_df$nzv |
(!is.na(plt_df$chisq.pval) & (plt_df$chisq.pval < 0.05)) &
(!is.na(plt_df$cor.y.abs) & (plt_df$cor.y.abs > .rnorm.cor.y.abs))
plt_df$label <- NA; plt_df[labelCnd, "label"] <- plt_df[labelCnd, "term"]
print(ggplot(plt_df, aes(x = cor.y, y = chisq.stat)) +
geom_point(aes(color = nzv.quad.fctr, size = weight)) +
geom_text(aes(label = label), color = "gray50") +
# geom_vline(xintercept = 0) +
geom_vline(xintercept = c(-1, +1) * .rnorm.cor.y.abs, color = "gray",
linetype = "dashed") +
ggtitle(txt_var))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glbFeatsText) {
print(sprintf("Extracting term weights for %s...", txt_var))
txt_corpus <- glb_txt_corpus_lst[[txt_var]]
full_DTM <- DocumentTermMatrix(txt_corpus,
control=glb_txt_terms_control)
sprs_DTM <- removeSparseTerms(full_DTM,
glb_sprs_thresholds[txt_var])
glb_full_DTM_lst[[txt_var]] <- full_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glbFeatsText) {
print(sprintf("Reporting term weights for %s...", txt_var))
full_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_DTM)
full_terms_df <- get_txt_terms(full_DTM)
# full_terms_df <- full_terms_df[, c(2, 1, 3, 4)]
# col_names <- names(full_terms_df)
# col_names[2:length(col_names)] <-
# paste(col_names[2:length(col_names)], ".full", sep="")
# names(full_terms_df) <- col_names
print(" Sparse TermMatrix:"); print(sprs_DTM)
sprs_terms_df <- get_txt_terms(sprs_DTM)
# sprs_terms_df <- sprs_terms_df[, c(2, 1, 3, 4)]
# col_names <- names(sprs_terms_df)
# col_names[2:length(col_names)] <-
# paste(col_names[2:length(col_names)], ".sprs", sep="")
# names(sprs_terms_df) <- col_names
#intersect(names(full_terms_df), names(sprs_terms_df))
terms_df <- merge(full_terms_df, sprs_terms_df, by = c("term", "weight", "freq",
grep("weight\\.", names(full_terms_df), value = TRUE)),
all.x = TRUE, suffixes = c(".full", ".sprs"))
terms_df$in.sprs <- !is.na(terms_df$pos.sprs)
plt_terms_df <- subset(terms_df,
weight >= min(terms_df$weight[!is.na(terms_df$pos.sprs)], na.rm=TRUE))
plt_terms_df$label <- ""
plt_terms_df[is.na(plt_terms_df$pos.sprs), "label"] <-
plt_terms_df[is.na(plt_terms_df$pos.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_terms_df[is.na(plt_terms_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_terms_df, "freq", "weight",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_terms_df <- orderBy(~ -value,
melt(terms_df, id.vars="term", measure.vars = c("weight", "freq")))
print(ggplot(melt_terms_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_terms_df <- orderBy(~ -value,
melt(subset(terms_df, in.sprs), id.vars="term",
measure.vars=grep("weight.", names(terms_df), value=TRUE)))
print(myplot_hbar(melt_terms_df, "term", "value", colorcol_name="variable"))
melt_terms_df <- orderBy(~ -value,
melt(subset(terms_df, !in.sprs), id.vars="term",
measure.vars=grep("weight.", names(terms_df), value=TRUE)))
print(myplot_hbar(head(melt_terms_df, glbFeatsTextTermsMax[[txt_var]]), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# print(identical(sav_glb_txt_corpus_lst, glb_txt_corpus_lst))
# print(all.equal(length(sav_glb_txt_corpus_lst), length(glb_txt_corpus_lst)))
# print(all.equal(names(sav_glb_txt_corpus_lst), names(glb_txt_corpus_lst)))
# print(all.equal(sav_glb_txt_corpus_lst[["Headline"]], glb_txt_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
rm(full_terms_mtrx)
# Create txt features
if ((length(glbFeatsText) > 1) &&
(length(unique(pfxs <- sapply(glbFeatsText,
function(txt) toupper(substr(txt, 1, 1))))) < length(glbFeatsText)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
require(tidyr)
for (txt_var in glbFeatsText) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_txt_terms(glb_full_DTM_lst[[txt_var]])
# make.names adds a period to R keywords e.g. "in", "function"
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_full_df <- terms_full_df
# names(plt_full_df)[grepl("weight$", names(plt_full_df))] <- "weight.all"
# # gather(plt_full_df[1:5, ], domain, TfIdf, -matches("!(TfIdf)"))
# # gather(plt_full_df[1:5, grepl("TfIdf", names(plt_full_df))], domain, TfIdf)
# # gather(plt_full_df[1:5, ], domain, TfIdf,
# # -names(plt_full_df)[!grepl("TfIdf", names(plt_full_df))])
# plt_full_df <- gather(plt_full_df, domain, weight,
# -c(term, freq, pos, cor.y, cor.y.abs))
# plt_full_df$label <- NA
# top_val_terms <- orderBy(~-weight, terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
# plt_full_df[plt_full_df$term %in% top_val_terms, "label"] <-
# plt_full_df[plt_full_df$term %in% top_val_terms, "term"]
# top_cor_terms <- orderBy(~-cor.y.abs,
# terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
# plt_full_df[plt_full_df$term %in% top_cor_terms, "label"] <-
# plt_full_df[plt_full_df$term %in% top_cor_terms, "term"]
# #plt_full_df$type <- "none"
# plt_full_df[plt_full_df$term %in% top_val_terms, "type"] <- "top.weight"
# plt_full_df[plt_full_df$term %in% top_cor_terms, "type"] <- "top.cor"
# plt_full_df[plt_full_df$term %in% intersect(top_val_terms, top_cor_terms), "type"] <-
# "top.both"
# cor.y.rnorm <- cor(glb_allobs_df$.rnorm, as.numeric(glb_allobs_df[, glb_rsp_var]),
# use = "pairwise.complete.obs")
# print(ggplot(plt_full_df, aes(x=weight, y=cor.y)) + facet_wrap(~ domain) +
# geom_point(aes(size=freq), color="grey") +
# geom_jitter() +
# geom_text(aes(label=label, color=type), size=3.5) +
# #geom_hline(yintercept=cor.y.rnorm, color="red") +
# geom_hline(yintercept=c(cor.y.rnorm, -cor.y.rnorm), color="red"))
#stop(here"); glb_to_sav()
if (glbFeatsTextFilter == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
select_terms <- make.names(colnames(txt_X_df))
# colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
# make.names(colnames(txt_X_df)), sep="")
# rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glbFeatsTextFilter == "top.val") {
select_terms <- orderBy(~-weight,
terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
# txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
# FALSE]
} else if (glbFeatsTextFilter == "top.cor") {
select_terms <- orderBy(~-cor.y.abs,
terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
# txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
# FALSE]
} else if (glbFeatsTextFilter == "top.chisq") {
select_terms <- orderBy(~-chisq.stat,
subset(terms_full_df, chisq.pval < 0.05)
)$term[1:glbFeatsTextTermsMax[[txt_var]]]
} else if (glbFeatsTextFilter == "union.top.val.cor") {
select_terms <- union(
orderBy(~-weight , terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]],
orderBy(~-cor.y.abs, terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]])
} else stop(
"glbFeatsTextFilter should be one of c('sparse', 'top.val', 'top.cor', 'union.top.val.cor', 'top.chisq') vs. '",
glbFeatsTextFilter, "'")
assoc_terms_lst <- findAssocs(glb_full_DTM_lst[[txt_var]], select_terms,
glbFeatsTextAssocCor[[txt_var]])
assoc_terms <- c(NULL)
for (term in names(assoc_terms_lst))
if (length(assoc_terms_lst[[term]]) > 0)
assoc_terms <- union(assoc_terms, names(assoc_terms_lst[[term]]))
#stop(here"); glb_to_sav()
txt_X_df <- txt_full_X_df[,
subset(terms_full_df, term %in% c(select_terms, assoc_terms))$pos,
FALSE]
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "\\^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glbFeatsText, .combine=cbind) %dopar% {
#for (txt_var in glbFeatsText) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.wrds.n.log & .wrds.unq.n.log
txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_chr_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".wrds.unq.n.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <-
txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")]),
paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <- 0
# Create <txt_var>.chrs.n.log
txt_X_df[, paste0(txt_var_pfx, ".chrs.n.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".chrs.uppr.n.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".dgts.n.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".chrs.pnct", sprintf("%02d", punct_ix), ".n.log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.wrds.stop.n.log & <txt_var>ratio.wrds.stop.n.wrds.n
if (!is.null(glb_txt_stop_words[[txt_var]])) {
stop_words_rex_str <- paste0("\\b(",
paste0(glb_txt_stop_words[[txt_var]], collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_chr_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.wrds.stop.n.wrds.n")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".wrds.n", ".log")])
}
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.black", sep="")] <-
as.integer(0 + mycount_pattern_occ("black", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.white", sep="")] <-
as.integer(0 + mycount_pattern_occ("white", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.gold", sep="")] <-
as.integer(0 + mycount_pattern_occ("gold", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.spacegray", sep="")] <-
as.integer(0 + mycount_pattern_occ("spacegray", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glbFeatsExclude <- union(glbFeatsExclude,
glbFeatsText)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr## label step_major step_minor label_minor
## 2 extract.features_factorize.str.vars 2 0 0
## 3 extract.features_process.text 3 0 0
## bgn end elapsed
## 2 25.047 25.102 0.055
## 3 25.103 NA NA
## [1] "Building glb_txt_chr_lst..."
## [1] "running gsub for 10 (of 179): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 179): #\\bAlejandro G\\. Iñárritu#..."
## [1] "running gsub for 30 (of 179): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 179): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 179): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 179): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 179): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 179): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 179): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 179): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 179): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 179): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 179): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 179): #\\bWall Street#..."
## [1] "running gsub for 150 (of 179): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 179): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 179): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"
## pattern .n
## 1 OK 1
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 CONDITION. 3
## 2 ICLOUD. 2
## 3 LOCKS. 2
## 4 ONLY. 2
## 5 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 2 New Screen 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
## pattern .n
## 1 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor label_minor bgn end elapsed
## 3 0 0 25.103 25.899 0.796
## 4 1 1 25.899 NA NA
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor label_minor bgn end elapsed
## 4 1 1 25.899 25.905 0.007
## 5 0 0 25.907 NA NA
## [1] "Building glb_txt_corpus_lst..."
## [1] " Top_n post-stop term weights for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## [1] "Rows: 301; Cols: 14"
## term weight freq pos cor.y cor.y.abs chisq.stat
## condition condition 78.98106 158 62 0.001344658 0.001344658 32.915492
## likenew likenew 56.21589 24 154 -0.065514942 0.065514942 8.821196
## excellent excellent 45.99670 45 97 0.047894495 0.047894495 7.927509
## used used 45.92642 97 283 0.017094877 0.017094877 34.012609
## good good 43.62859 67 117 0.016953140 0.016953140 13.151429
## and and 42.42133 130 20 0.006615581 0.006615581 19.047232
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y
## condition 0.04716262 66.63636 2.634731 TRUE 36.24096
## likenew 0.45394058 273.66667 1.197605 TRUE 14.90115
## excellent 0.84829064 202.75000 1.676647 TRUE 27.00915
## used 0.03612686 85.55556 2.634731 TRUE 24.88442
## good 0.72599123 131.00000 2.155689 TRUE 25.23756
## and 0.64241004 45.87500 2.754491 TRUE 24.09867
## weight.N weight.NA
## condition 11.062935 31.67717
## likenew 16.360991 24.95375
## excellent 1.741714 17.24584
## used 5.985050 15.05695
## good 6.040657 12.35037
## and 7.080998 11.24166
## term weight freq pos cor.y cor.y.abs
## broken broken 10.034760 7 40 -0.006934753 0.006934753
## never never 6.092265 7 176 -0.051660571 0.051660571
## open open 3.908698 4 187 0.001723551 0.001723551
## clear clear 3.584495 6 59 0.038525933 0.038525933
## previouslyused previouslyused 2.939749 6 218 0.038483567 0.038483567
## front front 2.509639 4 106 0.033041365 0.033041365
## chisq.stat chisq.pval nzv.freqRatio nzv.percentUnique nzv
## broken 7.4079876 0.1920224 830.0 0.7185629 TRUE
## never 7.8851891 0.1626776 414.5 0.7185629 TRUE
## open 3.8567029 0.2773592 832.0 0.4790419 TRUE
## clear 1.2410549 0.5376608 415.5 0.3592814 TRUE
## previouslyused 1.2410549 0.5376608 277.0 0.3592814 TRUE
## front 0.9296724 0.8182619 832.0 0.4790419 TRUE
## weight.Y weight.N weight.NA
## broken 5.823792 2.2959881 1.9149800
## never 2.447232 2.8189632 0.8260698
## open 2.397668 0.6868319 0.8241983
## clear 2.271863 0.0000000 1.3126321
## previouslyused 1.982621 0.0000000 0.9571276
## front 1.760368 0.0000000 0.7492712
## term weight freq pos cor.y cor.y.abs
## thats thats 0.6024696 1 269 -0.06231523 0.06231523
## conditions conditions 0.5689991 1 63 0.01924154 0.01924154
## opening opening 0.5689991 1 189 0.01924154 0.01924154
## edges edges 0.5390517 1 94 NA NA
## previously previously 0.5390517 1 216 NA NA
## seller seller 0.5390517 1 241 0.01924154 0.01924154
## chisq.stat chisq.pval nzv.freqRatio nzv.percentUnique nzv
## thats 3.873029e-01 0.5337205 834 0.2395210 TRUE
## conditions 2.732740e-30 1.0000000 834 0.2395210 TRUE
## opening 2.732740e-30 1.0000000 834 0.2395210 TRUE
## edges NA NA 0 0.1197605 TRUE
## previously NA NA 0 0.1197605 TRUE
## seller 2.732740e-30 1.0000000 834 0.2395210 TRUE
## weight.Y weight.N weight.NA
## thats 0.0000000 0.6024696 0.0000000
## conditions 0.5689991 0.0000000 0.0000000
## opening 0.5689991 0.0000000 0.0000000
## edges 0.0000000 0.0000000 0.5390517
## previously 0.0000000 0.0000000 0.5390517
## seller 0.5390517 0.0000000 0.0000000
## term weight freq pos cor.y cor.y.abs
## thats thats 0.6024696 1 269 -0.06231523 0.06231523
## conditions conditions 0.5689991 1 63 0.01924154 0.01924154
## opening opening 0.5689991 1 189 0.01924154 0.01924154
## edges edges 0.5390517 1 94 NA NA
## previously previously 0.5390517 1 216 NA NA
## seller seller 0.5390517 1 241 0.01924154 0.01924154
## chisq.stat chisq.pval nzv.freqRatio nzv.percentUnique nzv
## thats 3.873029e-01 0.5337205 834 0.2395210 TRUE
## conditions 2.732740e-30 1.0000000 834 0.2395210 TRUE
## opening 2.732740e-30 1.0000000 834 0.2395210 TRUE
## edges NA NA 0 0.1197605 TRUE
## previously NA NA 0 0.1197605 TRUE
## seller 2.732740e-30 1.0000000 834 0.2395210 TRUE
## weight.Y weight.N weight.NA
## thats 0.0000000 0.6024696 0.0000000
## conditions 0.5689991 0.0000000 0.0000000
## opening 0.5689991 0.0000000 0.0000000
## edges 0.0000000 0.0000000 0.5390517
## previously 0.0000000 0.0000000 0.5390517
## seller 0.5390517 0.0000000 0.0000000## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem term weights for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## [1] "Rows: 215; Cols: 14"
## term weight freq pos cor.y cor.y.abs chisq.stat
## condit condit 78.89912 159 50 0.001876192 0.001876192 32.693036
## likenew likenew 56.21589 24 111 -0.065514942 0.065514942 8.821196
## use use 51.89835 123 200 0.016242616 0.016242616 32.201419
## excel excel 46.05261 46 68 0.048677717 0.048677717 8.245331
## good good 43.62859 67 83 0.016953140 0.016953140 13.151429
## and and 42.42133 130 19 0.006615581 0.006615581 19.047232
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y weight.N
## condit 0.04973364 66.54545 2.634731 TRUE 36.29141 11.028663
## likenew 0.45394058 273.66667 1.197605 TRUE 14.90115 16.360991
## use 0.09602654 83.11111 2.874251 TRUE 27.96807 7.123860
## excel 0.82725815 202.50000 1.676647 TRUE 27.19181 1.730088
## good 0.72599123 131.00000 2.155689 TRUE 25.23756 6.040657
## and 0.64241004 45.87500 2.754491 TRUE 24.09867 7.080998
## weight.NA
## condit 31.57904
## likenew 24.95375
## use 16.80641
## excel 17.13071
## good 12.35037
## and 11.24166
## term weight freq pos cor.y cor.y.abs chisq.stat
## i i 9.1150671 18 90 0.01567642 0.01567642 3.715157e+00
## be be 7.4599452 16 29 -0.05024032 0.05024032 9.277724e+00
## kept kept 7.2385639 14 108 -0.01409382 0.01409382 1.266007e+01
## us us 6.1510383 12 198 0.03070196 0.03070196 9.623964e-01
## such such 3.3932004 8 185 -0.02931889 0.02931889 5.720570e+00
## 4g 4g 0.6401239 1 8 0.01924154 0.01924154 2.732740e-30
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y weight.N
## i 0.88186539 205.00 1.0778443 TRUE 6.4441552 1.4505472
## be 0.23332685 412.50 0.9580838 TRUE 2.4215714 1.8583159
## kept 0.08083691 164.60 0.9580838 TRUE 4.2514508 1.7104012
## us 0.81034983 118.00 0.4790419 TRUE 4.1027243 0.5120785
## such 0.12602540 206.75 0.4790419 TRUE 2.1625366 1.2306638
## 4g 1.00000000 834.00 0.2395210 TRUE 0.6401239 0.0000000
## weight.NA
## i 1.220365
## be 3.180058
## kept 1.276712
## us 1.536236
## such 0.000000
## 4g 0.000000
## term weight freq pos cor.y cor.y.abs chisq.stat
## b b 0.7315702 1 26 0.01924154 0.01924154 2.73274e-30
## 4g 4g 0.6401239 1 8 0.01924154 0.01924154 2.73274e-30
## heavili heavili 0.6401239 1 88 0.01924154 0.01924154 2.73274e-30
## ipad3 ipad3 0.6401239 1 98 0.01924154 0.01924154 2.73274e-30
## ship ship 0.6401239 1 171 0.01924154 0.01924154 2.73274e-30
## seller seller 0.5390517 1 168 0.01924154 0.01924154 2.73274e-30
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y weight.N
## b 1 834 0.239521 TRUE 0.7315702 0
## 4g 1 834 0.239521 TRUE 0.6401239 0
## heavili 1 834 0.239521 TRUE 0.6401239 0
## ipad3 1 834 0.239521 TRUE 0.6401239 0
## ship 1 834 0.239521 TRUE 0.6401239 0
## seller 1 834 0.239521 TRUE 0.5390517 0
## weight.NA
## b 0
## 4g 0
## heavili 0
## ipad3 0
## ship 0
## seller 0
## term weight freq pos cor.y cor.y.abs chisq.stat
## b b 0.7315702 1 26 0.01924154 0.01924154 2.73274e-30
## 4g 4g 0.6401239 1 8 0.01924154 0.01924154 2.73274e-30
## heavili heavili 0.6401239 1 88 0.01924154 0.01924154 2.73274e-30
## ipad3 ipad3 0.6401239 1 98 0.01924154 0.01924154 2.73274e-30
## ship ship 0.6401239 1 171 0.01924154 0.01924154 2.73274e-30
## seller seller 0.5390517 1 168 0.01924154 0.01924154 2.73274e-30
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y weight.N
## b 1 834 0.239521 TRUE 0.7315702 0
## 4g 1 834 0.239521 TRUE 0.6401239 0
## heavili 1 834 0.239521 TRUE 0.6401239 0
## ipad3 1 834 0.239521 TRUE 0.6401239 0
## ship 1 834 0.239521 TRUE 0.6401239 0
## seller 1 834 0.239521 TRUE 0.5390517 0
## weight.NA
## b 0
## 4g 0
## heavili 0
## ipad3 0
## ship 0
## seller 0## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.post.stop.n -0.007640123
## terms.post.stop.n.log 0.012379490
## weight.post.stop.sum 0.024438632
## terms.post.stem.n -0.007414802
## terms.post.stem.n.log 0.012404084
## weight.post.stem.sum 0.021800973
## terms.n.stem.stop.Ratio -0.001258953
## weight.sum.stem.stop.Ratio -0.051726793## Loading required package: wordcloud
## Loading required package: RColorBrewer## [1] " Wordcloud post-stem term weights for descr.my:"
## Warning: Removed 207 rows containing missing values (geom_text).## label step_major step_minor label_minor bgn
## 5 extract.features_build.corpus 4 0 0 25.907
## 6 extract.features_extract.DTM 5 0 0 39.502
## end elapsed
## 5 39.501 13.594
## 6 NA NA
## [1] "Extracting term weights for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor label_minor bgn
## 6 extract.features_extract.DTM 5 0 0 39.502
## 7 extract.features_report.DTM 6 0 0 40.203
## end elapsed
## 6 40.203 0.701
## 7 NA NA
## [1] "Reporting term weights for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 1211, terms: 215)>>
## Non-/sparse entries: 4218/256147
## Sparsity : 98%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 1211, terms: 17)>>
## Non-/sparse entries: 1657/18930
## Sparsity : 92%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in myplot_scatter(plt_terms_df, "freq", "weight", colorcol_name =
## "in.sprs"): converting in.sprs to class:factor
## Warning in rm(full_terms_mtrx): object 'full_terms_mtrx' not found## label step_major step_minor label_minor bgn
## 7 extract.features_report.DTM 6 0 0 40.203
## 8 extract.features_bind.DTM 7 0 0 43.578
## end elapsed
## 7 43.577 3.374
## 8 NA NA## Loading required package: tidyr## [1] "Binding DTM for descr.my..."## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## label step_major step_minor label_minor bgn
## 8 extract.features_bind.DTM 7 0 0 43.578
## 9 extract.features_bind.DXM 8 0 0 45.245
## end elapsed
## 8 45.245 1.667
## 9 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
# Use model info provided in description
# mydsp_obs(list(description.contains="a[[:digit:]]"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "prdline.my"] <- "iPad mini"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "color"] <- "Space Gray"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "carrier"] <- "None"
#
# mydsp_obs(list(description.contains="m(.{4})ll"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "color"] <- "Black"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "storage"] <- "64"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "carrier"] <- "None"
#
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "prdline.my"] <- "iPad Air"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "color"] <- "White"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "carrier"] <- "None"
# mydsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# mydsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# mydsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini",
# glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
# (glb_allobs_df$D.P.mini > 0),
# c(glb_id_var, glb_category_var, glb_dsp_cols, glbFeatsText)])
# glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"),
# "prdline.my"] <- "iPad mini"
# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air",
# glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
# (glb_allobs_df$D.P.air > 0),
# c(glb_id_var, glb_category_var, glb_dsp_cols, glbFeatsText)])
# #glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
# glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"),
# "prdline.my"] <- "iPad Air"
# print(glb_allobs_df[(glb_allobs_df$UniqueID %in% c(11767, 11811, 12156)),
# c(glb_id_var, "sold",
# "prdline.my", "color", "condition", "cellular", "carrier", "storage"
# #, "descr.my"
# )])
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
# mydsp_obs(list(prdline.my="Unknown"), all=TRUE)
# tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
# names(tmp_allobs_df) <- "old.prdline.my"
# glb_allobs_df$prdline.my <-
# plyr::revalue(glb_allobs_df$prdline.my, c(
# # "iPad 1" = "iPad",
# # "iPad 2" = "iPad2+",
# "iPad 3" = "iPad 3+",
# "iPad 4" = "iPad 3+",
# "iPad 5" = "iPad 3+",
#
# "iPad Air" = "iPadAir",
# "iPad Air 2" = "iPadAir",
#
# "iPad mini" = "iPadmini",
# "iPad mini 2" = "iPadmini 2+",
# "iPad mini 3" = "iPadmini 2+",
# "iPad mini Retina" = "iPadmini 2+"
# ))
# tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))
# print(mycreate_sqlxtab_df(subset(glb_allobs_df, color == "Unknown"),
# c("color", "D.P.black", "D.P.gold", "D.P.spacegray", "D.P.white")))
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.black > 0),
# c(glb_id_var, "color", "D.P.black", "sold", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID == 12137, "color"] <- "Black"
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.spacegray > 0),
# c(glb_id_var, "color", "D.P.spacegray", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(12106), "color"] <- "Space Gray"
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.white > 0),
# c(glb_id_var, "color", "D.P.white", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(10573, 10809, 10925, 11735), "color"] <-
# "White"
glb_allobs_df$carrier.fctr <- as.factor(glb_allobs_df$carrier)
glb_allobs_df$cellular.fctr <- as.factor(glb_allobs_df$cellular)
glb_allobs_df$color.fctr <- as.factor(glb_allobs_df$color)
# glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# glb_allobs_df %>%
# unite(prdl.descr.my, c(productline, as.numeric(D.chrs.n.log > 0), sep="#"))
# unite_("prdl.descr.my", interp(~c("productline", as.numeric(D.chrs.n.log > 0), sep="#")))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
if (!is.null(glbFeatsPrice)) {
for (var in glbFeatsPrice) {
for (digit in 1:(log10(max(glb_allobs_df[, var], na.rm=TRUE)) + 1)) {
glb_allobs_df[, paste0(var, ".dgt", digit, ".is9")] <-
as.numeric(as.integer((as.integer(glb_allobs_df[, var]) %% (10 ^ digit)) /
(10 ^ (digit - 1))) == 9)
# glb_allobs_df[, paste0(var, ".dgt", digit, ".is9.fctr")] <-
# as.factor(as.integer((as.integer(glb_allobs_df[, var]) %% (10 ^ digit)) /
# (10 ^ (digit - 1))) == 9)
}
for (decimal in 1:2) {
glb_allobs_df[, paste0(var, ".dcm", decimal, ".is9")] <-
as.numeric(as.integer(glb_allobs_df[, var] * (10 ^ decimal)) %% 10 == 9)
# glb_allobs_df[, paste0(var, ".dcm", decimal, ".is9.fctr")] <-
# as.factor(as.integer(glb_allobs_df[, var] * (10 ^ decimal)) %% 10 == 9)
}
}
#as.numeric((as.integer(startprice) %% 10) == 9)
}
rm(corpus_lst
, glb_sprs_DTM_lst #, glb_full_DTM_lst
, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr): object
## 'corpus_lst' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor label_minor bgn
## 9 extract.features_bind.DXM 8 0 0 45.245
## 10 extract.features_end 9 0 0 128.598
## end elapsed
## 9 128.597 83.353
## 10 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 8 extract.features_bind.DTM 7
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor label_minor bgn end elapsed duration
## 9 0 0 45.245 128.597 83.353 83.352
## 5 0 0 25.907 39.501 13.594 13.594
## 7 0 0 40.203 43.577 3.374 3.374
## 8 0 0 43.578 45.245 1.667 1.667
## 3 0 0 25.103 25.899 0.796 0.796
## 6 0 0 39.502 40.203 0.701 0.701
## 2 0 0 25.047 25.102 0.055 0.055
## 1 0 0 25.032 25.047 0.015 0.015
## 4 1 1 25.899 25.905 0.007 0.006
## [1] "Total Elapsed Time: 128.597 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
#glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=TRUE)Step 3.0: extract features
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 5 extract.features 3 0 0 25.025 133.404
## 6 manage.missing.data 3 1 1 133.405 NA
## elapsed
## 5 108.38
## 6 NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals)## [1] "numeric data missing in : "
## sold sold.fctr
## 376 376
## [1] "numeric data w/ 0s in : "
## sold sprice.log10
## 197 31
## cellular.fctr D.terms.post.stop.n
## 789 754
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 754 754
## D.terms.post.stem.n D.terms.post.stem.n.log
## 754 754
## D.weight.post.stem.sum D.T.condit
## 754 1052
## D.T.mint D.T.for.
## 1192 1170
## D.T.read D.T.wifi
## 1194 1196
## D.T.full D.T.usb
## 1202 1204
## D.T.X32gb D.T.part
## 1203 1206
## D.T.unlock D.T.adapt
## 1207 1206
## D.T.at D.wrds.n.log
## 1207 753
## D.wrds.unq.n.log D.weight.sum
## 754 754
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 754 753
## D.chrs.uppr.n.log D.dgts.n.log
## 753 1159
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 1173 1211
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 1211 1211
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 1204 1206
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 1191 1194
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 1210 1211
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 1060 1165
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 923 1191
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 1205 1207
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 1210 1211
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 1211 1211
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 1211 1211
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 1211 1211
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 1211 1211
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 1211 1211
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 1211 1211
## D.wrds.stop.n.log D.P.http
## 923 1211
## D.P.mini D.P.air
## 1197 1202
## D.P.black D.P.white
## 1199 1206
## D.P.gold D.P.spacegray
## 1211 1207
## startprice.dgt1.is9 startprice.dgt2.is9
## 908 1064
## startprice.dgt3.is9 startprice.dcm1.is9
## 1211 775
## startprice.dcm2.is9
## 830
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid descr.my
## 0 NA 753# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glbFeatsExclude, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glbFeatsExclude <- c(glbFeatsExclude, col)
}
}
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 376 376
## [1] "numeric data w/ 0s in : "
## sold sprice.log10
## 197 31
## cellular.fctr D.terms.post.stop.n
## 789 754
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 754 754
## D.terms.post.stem.n D.terms.post.stem.n.log
## 754 754
## D.weight.post.stem.sum D.T.condit
## 754 1052
## D.T.mint D.T.for.
## 1192 1170
## D.T.read D.T.wifi
## 1194 1196
## D.T.full D.T.usb
## 1202 1204
## D.T.X32gb D.T.part
## 1203 1206
## D.T.unlock D.T.adapt
## 1207 1206
## D.T.at D.wrds.n.log
## 1207 753
## D.wrds.unq.n.log D.weight.sum
## 754 754
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 754 753
## D.chrs.uppr.n.log D.dgts.n.log
## 753 1159
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 1173 1211
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 1211 1211
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 1204 1206
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 1191 1194
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 1210 1211
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 1060 1165
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 923 1191
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 1205 1207
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 1210 1211
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 1211 1211
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 1211 1211
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 1211 1211
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 1211 1211
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 1211 1211
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 1211 1211
## D.wrds.stop.n.log D.P.http
## 923 1211
## D.P.mini D.P.air
## 1197 1202
## D.P.black D.P.white
## 1199 1206
## D.P.gold D.P.spacegray
## 1211 1207
## startprice.dgt1.is9 startprice.dgt2.is9
## 908 1064
## startprice.dgt3.is9 startprice.dcm1.is9
## 1211 775
## startprice.dcm2.is9
## 830
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid descr.my
## 0 NA 753Step 3.1: manage missing data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 6 manage.missing.data 3 1 1 133.405 133.547
## 7 cluster.data 3 2 2 133.547 NA
## elapsed
## 6 0.142
## 7 NAmycompute_entropy_df <- function(obs_df, entropy_var, by_var=NULL) {
require(lazyeval)
require(dplyr)
require(tidyr)
if (is.null(by_var)) {
by_var <- ".default"
obs_df$.default <- as.factor(".default")
}
if (!any(grepl(".clusterid", names(obs_df), fixed=TRUE)))
obs_df$.clusterid <- 1
cluster_df <- obs_df %>%
count_(c(by_var, ".clusterid", entropy_var)) %>%
dplyr::filter(n > 0) %>%
dplyr::filter_(interp(~(!is.na(var)), var=as.name(entropy_var))) %>%
unite_(paste0(by_var, ".clusterid"),
c(interp(by_var), ".clusterid")) %>%
spread_(interp(entropy_var), "n", fill=0)
# head(cluster_df)
# sum(cluster_df$n)
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method = "ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
#print(cluster_df)
return(cluster_df)
}
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
require(ggdendro)
mywgtdcosine_dist <- function(x, y=NULL, weights=NULL) {
if (!inherits(x, "matrix"))
x <- as.matrix(x)
if (is.null(weights))
weights <- rep(1, ncol(x))
wgtsx <- matrix(rep(weights / sum(weights), nrow(x)), nrow = nrow(x),
byrow = TRUE)
wgtdx <- x * wgtsx
wgtdxsqsum <- as.matrix(rowSums((x ^ 2) * wgtsx), byrow=FALSE)
denom <- sqrt(wgtdxsqsum %*% t(wgtdxsqsum))
ret_mtrx <- 1 - ((sum(weights) ^ 1) * (wgtdx %*% t(wgtdx)) / denom)
ret_mtrx[is.nan(ret_mtrx)] <- 1
diag(ret_mtrx) <- 0
return(ret_mtrx)
}
#pr_DB$delete_entry("mywgtdcosine");
# Need to do this only once across runs ?
if (!pr_DB$entry_exists("mywgtdcosine")) {
pr_DB$set_entry(FUN = mywgtdcosine_dist, names = c("mywgtdcosine"))
pr_DB$modify_entry(names="mywgtdcosine", type="metric", loop=FALSE)
}
#pr_DB$get_entry("mywgtdcosine")
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
"]\\.[PT]\\."),
names(glb_allobs_df), value = TRUE)
# Assign correlations with rsp_var as weights for cosine distance
print("Clustering features: ")
cluster_vars_df <- data.frame(abs.cor.y = abs(cor(
glb_allobs_df[glb_allobs_df$.src == "Train", cluster_vars],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs")))
print(tail(cluster_vars_df <- orderBy(~ abs.cor.y,
subset(cluster_vars_df, !is.na(abs.cor.y))), 5))
print(sprintf(" .rnorm cor: %0.4f",
cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs")))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_cluster_entropy_var]),
method="ML")))
print(category_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
entropy_var=glb_cluster_entropy_var,
by_var=glb_category_var))
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)",
glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
#stop(here"); glb_to_sav()
grp_ids <- sort(unique(glb_allobs_df[, glb_category_var]))
glb_cluster_size_df_lst <- list()
png(paste0(glb_out_pfx, "FeatsTxtClusters.png"),
width = 480 * 2, height = 480 * length(grp_ids))
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow = length(grp_ids), ncol = 2)))
pltIx <- 1
for (grp in grp_ids) {
# if (grep(grp, levels(grp_ids)) <= 6) next
# if (grep(grp, levels(grp_ids)) > 9) next
# if (grep(grp, levels(grp_ids)) != 10) next
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
if (!inherits(ctgry_allobs_df[, glb_cluster_entropy_var], "factor"))
ctgry_allobs_df[, glb_cluster_entropy_var] <-
as.factor(ctgry_allobs_df[, glb_cluster_entropy_var])
#dstns_dist <- proxy::dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_dist <- proxy::dist(ctgry_allobs_df[, row.names(cluster_vars_df)],
method = "mywgtdcosine",
weights = cluster_vars_df$abs.cor.y)
# Custom distance functions return a crossdist object
#dstns_mtrx <- as.matrix(dstns_dist)
dstns_mtrx <- matrix(as.vector(dstns_dist), nrow=attr(dstns_dist, "dim")[1],
dimnames=attr(dstns_dist, "dimnames"))
dstns_dist <- as.dist(dstns_mtrx)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
# print(dim(dstns_mtrx))
# print(sprintf("which.max: %d", which.max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
# print(sprintf("row_ix: %d", row_ix)); print(sprintf("col_ix: %d", col_ix));
# print(dim(ctgry_allobs_df))
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glbFeatsText, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
# Float representations issue -2.22e-16 vs. 0.0000
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glbFeatsText,
cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
# Workaround to avoid "Error in cutree(dendro, h = heightcutoff) : the 'height' component of 'tree' is not sorted (increasingly)"
if (with(clusters,all.equal(height,sort(height))))
clusters$height <- round(clusters$height,6)
clusters$labels <- ctgry_allobs_df[, glb_id_var]
clustersDD <- dendro_data(clusters)
clustersDD$labels[, glb_rsp_var] <- sapply(clustersDD$labels$label, function(id)
ctgry_allobs_df[id == ctgry_allobs_df[, glb_id_var], glb_rsp_var])
print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
geom_point(data = clustersDD$labels,
aes_string(x = "x", color = glb_rsp_var), y = min(clustersDD$segments$y)) +
coord_flip(ylim = c(min(clustersDD$segments$y),
max(clustersDD$segments$y))) +
ggtitle(grp),
vp = viewport(layout.pos.row = pltIx, layout.pos.col = 1))
# clusters$labels <- ctgry_allobs_df[, glb_id_var]
# clustersDD <- dendro_data(clusters)
# clustersDD$labels$color <- sapply(clustersDD$labels$label, function(id)
# ctgry_allobs_df[id == ctgry_allobs_df[, glb_id_var], glb_rsp_var])
# print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
# geom_point(data = clustersDD$labels,
# aes_string(x = "x", color = "color"), y = min(clustersDD$segments$y)) +
# coord_flip(ylim = c(min(clustersDD$segments$y),
# max(clustersDD$segments$y))))
# print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
# geom_point(data = clustersDD$labels,
# aes_string(x = "x", y = "y", color = "color")))
# myplclust(clusters, lab=ctgry_allobs_df[, glb_id_var],
# lab.col=unclass(ctgry_allobs_df[, glb_cluster_entropy_var]))
opt_minclustersize_df <- data.frame(minclustersize = nrow(ctgry_allobs_df),
entropy = entropy(table(ctgry_allobs_df[, glb_cluster_entropy_var]),
method = "ML"))
for (minclustersize in
as.integer(seq(nrow(ctgry_allobs_df) / 2, nrow(ctgry_allobs_df) / 10,
length = 5))) {
clusterGroups <- cutreeDynamic(clusters, minClusterSize = minclustersize,
method = "tree", deepSplit = 0)
# Unassigned groups are labeled 0; the largest group has label 1
clusterGroups[clusterGroups == 0] <- 1
ctgry_allobs_df$.clusterid <- clusterGroups
ctgry_clstrs_df <- mycompute_entropy_df(ctgry_allobs_df,
glb_cluster_entropy_var)
opt_minclustersize_df <- rbind(opt_minclustersize_df,
data.frame(minclustersize = minclustersize,
entropy = weighted.mean(ctgry_clstrs_df$.entropy, ctgry_clstrs_df$.knt)))
}
opt_minclustersize <-
opt_minclustersize_df$minclustersize[which.min(opt_minclustersize_df$entropy)]
opt_minclustersize_df$.color <-
ifelse(opt_minclustersize_df$minclustersize == opt_minclustersize,
"red", "blue")
print(ggplot(data = opt_minclustersize_df,
mapping = aes(x = minclustersize, y = entropy)) +
geom_point(aes(color = .color)) + scale_color_identity() +
guides(color = "none") + geom_line(),
vp = viewport(layout.pos.row = pltIx, layout.pos.col = 2))
glb_cluster_size_df_lst[[grp]] <- opt_minclustersize_df
# select minclustersize that minimizes entropy
clusterGroups <- cutreeDynamic(clusters, minClusterSize = opt_minclustersize,
method = "tree",
deepSplit = 0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var],
useNA = "ifany")
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA = "ifany")
glb_allobs_df[glb_allobs_df[, glb_category_var] == grp,]$.clusterid <-
clusterGroups
pltIx <- pltIx + 1
}
dev.off()
#all.equal(sav_allobs_df_clusterid, glb_allobs_df$.clusterid)
print(cluster_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
entropy_var=glb_cluster_entropy_var,
by_var=glb_category_var))
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
# .clusterid.fctr is created automatically (probably ?) later
glbFeatsExclude <- c(glbFeatsExclude, ".clusterid")
if (!is.null(glb_category_var))
# glb_interaction_only_feats_lst[ifelse(grepl("\\.fctr", glb_category_var),
# glb_category_var,
# paste0(glb_category_var, ".fctr"))] <-
# c(".clusterid.fctr")
glb_interaction_only_feats_lst[[".clusterid.fctr"]] <-
ifelse(grepl("\\.fctr", glb_category_var), glb_category_var,
paste0(glb_category_var, ".fctr"))
if (glbFeatsTextClusterVarsExclude)
glbFeatsExclude <- c(glbFeatsExclude, cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: ggdendro## [1] "Clustering features: "## Warning in cor(glb_allobs_df[glb_allobs_df$.src == "Train",
## cluster_vars], : the standard deviation is zero## abs.cor.y
## D.T.wifi 0.05766683
## D.T.adapt 0.06014555
## D.T.X32gb 0.06556183
## D.T.at 0.07018402
## D.T.full 0.12093514
## [1] " .rnorm cor: -0.0412"
## [1] "glb_allobs_df Entropy: 0.5463"## Loading required package: lazyeval## Source: local data frame [20 x 5]
##
## prdl.descr.my.fctr.clusterid N Y .entropy .knt
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 Unknown#0_1 20 32 0.6662784 52
## 2 Unknown#1_1 8 24 0.5623351 32
## 3 iPad1#0_1 12 55 0.4700328 67
## 4 iPad1#1_1 10 41 0.4949177 51
## 5 iPad2#0_1 16 67 0.4902162 83
## 6 iPad2#1_1 9 48 0.4361623 57
## 7 iPad3#0_1 9 43 0.4607317 52
## 8 iPad3#1_1 5 20 0.5004024 25
## 9 iPad4#0_1 9 29 0.5474125 38
## 10 iPad4#1_1 10 16 0.6662784 26
## 11 iPadAir#0_1 7 33 0.4637264 40
## 12 iPadAir#1_1 6 28 0.4659993 34
## 13 iPadAir2#0_1 13 35 0.5840859 48
## 14 iPadAir2#1_1 2 16 0.3488321 18
## 15 iPadmini#0_1 23 59 0.5934134 82
## 16 iPadmini#1_1 14 40 0.5722807 54
## 17 iPadmini2#0_1 7 22 0.5526646 29
## 18 iPadmini2#1_1 5 12 0.6057975 17
## 19 iPadmini3#0_1 10 15 0.6730117 25
## 20 iPadmini3#1_1 2 3 0.6730117 5
## [1] "glb_allobs_df$prdl.descr.my.fctr Entropy: 0.5321 (97.3935 pct)"## [1] "Category: Unknown#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 47 10145 Y Unknown#0 0 0
## 85 10211 Y Unknown#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 47 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 47 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 47 0 0 0
## 85 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 47 10145 Y Unknown#0 0 0
## 47.1 10145 Y Unknown#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 47 0 0 0 0 0 0 0
## 47.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 47 0 0 0 0 0 0 0
## 47.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 47 0 0 0
## 47.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: Unknown#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 38 10130 Y Unknown#1
## 78 10199 N Unknown#1
## descr.my D.T.condit
## 38 New - Open Box. Charger included. 0.0000000
## 78 In great working condition. Very little where in tear. 0.3661375
## D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 38 0 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 38 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 38 0 0 0
## 78 0 0 0
## [1] "min distance(0.9670) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 233 10420 Y Unknown#1
## 608 11127 N Unknown#1
## descr.my
## 233 THIS ITEM WAS A STORE DEMO BUT HAS 100% BEEN RESTORED TO FACTORY SETTINGS AND IS READY FOR USE. DOES
## 608 Only used for a few months has always had a screen protector and case on it, so it is flawless!
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 233 0 0 0.2873195 0 0 0 0
## 608 0 0 0.2570753 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 233 0 0 0 0 0 0 0
## 608 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 233 0 0 0 0 0
## 608 0 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPad1#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 4 10009 Y iPad1#0 0 0
## 16 10074 Y iPad1#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 4 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 4 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 4 0 0 0
## 16 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 4 10009 Y iPad1#0 0 0
## 4.1 10009 Y iPad1#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 4 0 0 0 0 0 0 0
## 4.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 4 0 0 0 0 0 0 0
## 4.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 4 0 0 0
## 4.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad1#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 6 10013 Y iPad1#1
## 42 10138 Y iPad1#1
## descr.my
## 6 GOOD CONDITION ..CLEAN ICLOUD..NO LOCKS..CLEAN IMEI..This tablet has been fully tested and works
## 42 Works great. I dinged corner. Doesn't affect use. If you add a case ding is completely unnoticeable.
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 6 0.1830688 0 0 0 0 0 0
## 42 0.0000000 0 0 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 6 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 6 0 0 0 0 0
## 42 0 0 0 0 0
## [1] "min distance(0.9615) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 304 10526 N iPad1#1
## 417 10706 N iPad1#1
## descr.my
## 304 Mint condition, no problem, excellent condition
## 417 Ipad was only used a few times and is in mint likenew condition. Comes with a high speed charger,
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 304 1.17164 1.1988111 0 0 0 0 0
## 417 0.17230 0.3525915 0 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 304 0 0 0 0 0 0 0
## 417 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 304 0 0 0 0 0
## 417 0 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPad2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 15 10073 Y iPad2#0 0 0
## 19 10081 Y iPad2#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 15 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 15 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 15 0 0 0
## 19 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 15 10073 Y iPad2#0 0 0
## 15.1 10073 Y iPad2#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 15 0 0 0 0 0 0 0
## 15.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 15 0 0 0 0 0 0 0
## 15.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 15 0 0 0
## 15.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 1 10002 Y iPad2#1
## 49 10151 Y iPad2#1
## descr.my
## 1 Previouslyused , please read description. May show signs of use such as scratches to the screen and
## 49 This iPad2 is iCloud locked I repeat it is iCloud locked if you can get the iCloud lock off of it
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 1 0 0 0 0.3846575 0 0 0
## 49 0 0 0 0.0000000 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 1 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 1 0 0 0 0 0
## 49 0 0 0 0 0
## [1] "min distance(0.9583) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 400 10683 N iPad2#1
## 1206 12639 <NA> iPad2#1
## descr.my
## 400 Comes with folding black case and is engraved in small letters on the back. Still works perfectly
## 1206 IPAD2 64GB BLACK MODEL MC916LL/A WIFIONLY MODEL. PICTURE OF IPAD IS ACTUAL UNIT YOU WILL RECEIVE.
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 400 0 0 0 0 0 0 0
## 1206 0 0 0 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 400 0 0 0 0 0 0 0
## 1206 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 400 0 1 0 0 0
## 1206 0 1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPad3#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 24 10097 Y iPad3#0 0 0
## 35 10124 Y iPad3#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 24 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 24 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 24 0 0 0
## 35 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 24 10097 Y iPad3#0 0 0
## 24.1 10097 Y iPad3#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 24 0 0 0 0 0 0 0
## 24.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 24 0 0 0 0 0 0 0
## 24.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 24 0 0 0
## 24.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad3#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 14 10070 Y iPad3#1
## 25 10098 Y iPad3#1
## descr.my
## 14 Heavily used, digitizer is cracked in top right corner, plastic cover over 4G antenna has a crack in
## 25 Cracked screen and locked
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 14 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 14 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 14 0 0 0 0 0
## 25 0 0 0 0 0
## [1] "min distance(0.9981) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 50 10152 Y iPad3#1
## 122 10271 Y iPad3#1
## descr.my
## 50 Includes iPad, charger, wall plug, and Apple iPad Smart Case. Front screen excellent condition
## 122 Excellent Condition
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 50 0.2440917 0 0 0 0 0 0
## 122 1.4645501 0 0 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 50 0 0 0 0 0 0 0
## 122 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 50 0 0 0 0 0
## 122 0 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad4#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 5 10010 Y iPad4#0 0 0
## 36 10125 Y iPad4#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 5 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 5 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 5 0 0 0
## 36 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 5 10010 Y iPad4#0 0 0
## 5.1 10010 Y iPad4#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 5 0 0 0 0 0 0 0
## 5.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 5 0 0 0 0 0 0 0
## 5.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 5 0 0 0
## 5.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad4#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 8 10049 Y iPad4#1
## 81 10203 Y iPad4#1
## descr.my
## 8 great shape
## 81 iPad is in great condition. Works perfect, still as fast a day one. There is normal wear and tear
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 8 0.0000000 0 0 0 0 0 0
## 81 0.1627278 0 0 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 8 0 0 0 0 0 0 0
## 81 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 8 0 0 0 0 0
## 81 0 0 0 0 0
## [1] "min distance(0.9482) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 310 10532 N iPad4#1
## 426 10722 N iPad4#1
## descr.my
## 310 iPad has been tested and is in good working condition; Includes AC adapter USB cable; Screen and
## 426 Includes AC adapter, case, screen protector and box; Case and screen protector have scuffs and
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 310 0.1830688 0 0 0 0 0 0.4646643
## 426 0.0000000 0 0 0 0 0 0.0000000
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 310 0 0 0 0.4950034 0 0 0
## 426 0 0 0 0.5657182 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 310 0 0 0 0 0
## 426 0 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPadAir#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 7 10034 Y iPadAir#0 0 0
## 9 10050 Y iPadAir#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 7 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 7 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 7 0 0 0
## 9 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 7 10034 Y iPadAir#0 0 0
## 7.1 10034 Y iPadAir#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 7 0 0 0 0 0 0 0
## 7.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 7 0 0 0 0 0 0 0
## 7.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 7 0 0 0
## 7.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadAir#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 30 10114 Y iPadAir#1
## 32 10119 Y iPadAir#1
## descr.my
## 30 comes with: grey ipad divider case, white stylus, Apple USB wall charger and USB cord. HDMI adapter
## 32 Brandnew unopened still sealed ipadair2.
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 30 0 0 0 0 0 0 1.239105
## 32 0 0 0 0 0 0 0.000000
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 30 0 0 0 0.6600046 0 0 0
## 32 0 0 0 0.0000000 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 30 0 0 1 0 0
## 32 1 0 0 0 0
## [1] "min distance(0.9108) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 447 10755 Y iPadAir#1
## 546 10938 N iPadAir#1
## descr.my
## 447 Good working and fully functional 16GB iPadAir Wifi & cellular. Please read full discription
## 546 Good working condition and fully functional 16GB iPadAir Wifi. Please read full discription
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 447 0.0000000 0 0 0.5128767 0.5279244 0.5893382 0
## 546 0.2253154 0 0 0.4734246 0.4873148 0.5440045 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 447 0 0 0 0 0 0 0
## 546 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 447 1 0 0 0 0
## 546 1 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "Category: iPadAir2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 20 10090 Y iPadAir2#0 0 0
## 22 10092 Y iPadAir2#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 20 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 20 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 20 0 0 0
## 22 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 20 10090 Y iPadAir2#0 0 0
## 20.1 10090 Y iPadAir2#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 20 0 0 0 0 0 0 0
## 20.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 20 0 0 0 0 0 0 0
## 20.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 20 0 0 0
## 20.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadAir2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 77 10198 Y iPadAir2#1
## 175 10342 Y iPadAir2#1
## descr.my
## 77 Clean ESN. iCloud clear. No charger is included. Very good condition. See pictures.
## 175 PreviouslyOwned . Please read the entire auction. Tablet has only a very light scratches on back of
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 77 0.2253154 0 0 0.0000000 0 0 0
## 175 0.0000000 0 0 0.4396086 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 77 0 0 0 0 0 0 0
## 175 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 77 0 0 0 0 0
## 175 0 0 0 0 0
## [1] "min distance(0.9673) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 96 10231 Y iPadAir2#1
## 493 10827 Y iPadAir2#1
## descr.my
## 96 iPad is in GREAT condition! The screen and case has a few light surface scratches. See photos for
## 493 This iPad is set back to factory settings with NO ICLOUD! Ready for anyone to setup! Comes in
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 96 0.1723 0 0.2873195 0 0 0 0
## 493 0.0000 0 0.3052769 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 96 0 0 0 0 0 0 0
## 493 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 96 0 0 0 0 0
## 493 0 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPadmini#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 3 10007 Y iPadmini#0 0 0
## 17 10075 Y iPadmini#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 3 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 3 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 3 0 0 0
## 17 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 3 10007 Y iPadmini#0 0 0
## 3.1 10007 Y iPadmini#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 3 0 0 0 0 0 0 0
## 3.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 3 0 0 0 0 0 0 0
## 3.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 3 0 0 0
## 3.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 29 10113 Y iPadmini#1
## 59 10174 Y iPadmini#1
## descr.my
## 29 READ iPad is used but in excellent condition. Absolutely nothing wrong with it and it has recently
## 59 Please see images and refer closely to the description.
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 29 0.1952733 0 0 0.4103014 0 0 0
## 59 0.0000000 0 0 0.0000000 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 29 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 29 0 0 0 0 0
## 59 0 0 0 0 0
## [1] "min distance(0.9295) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 306 10528 N iPadmini#1
## 1194 12601 <NA> iPadmini#1
## descr.my
## 306 Good condition and functional 16GB iPadMini Wifi - White. Please read full discription
## 1194 Front screen is broken. Is in full working condition. Works great!
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 306 0.2440917 0 0 0.5128767 0.5279244 0.5893382 0
## 1194 0.2662818 0 0 0.0000000 0.0000000 0.6429144 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 306 0 0 0 0 0 0 1
## 1194 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 306 0 0 1 0 0
## 1194 0 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPadmini2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 2 10006 Y iPadmini2#0 0 0
## 10 10056 Y iPadmini2#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 2 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 2 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 2 0 0 0
## 10 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 2 10006 Y iPadmini2#0 0 0
## 2.1 10006 Y iPadmini2#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 2 0 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 2 0 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 2 0 0 0
## 2.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 26 10103 N iPadmini2#1
## 335 10573 Y iPadmini2#1
## descr.my
## 26 Has a small crack on top right corner
## 335 Likenew white iPadmini no scratches always kept in case, sold with keyboard, box and cords
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 26 0 0 0 0 0 0 0
## 335 0 0 0 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 26 0 0 0 0 0 0 0
## 335 0 0 0 0 0 0 1
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 26 0 0 0 0 0
## 335 0 0 1 0 0
## [1] "min distance(0.9588) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 1105 12331 <NA> iPadmini2#1
## 1155 12504 <NA> iPadmini2#1
## descr.my
## 1105 The iPadmini2 has absolutely no physical wear, scratches, dents, or technical issues of any kind,
## 1155 This iPadmini is brandnew I had it for about a month used it maybe three times.it still has the
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 1105 0 0 0.0000000 0 0 0 0
## 1155 0 0 0.2873195 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 1105 0 0 0 0 0 0 1
## 1155 0 0 0 0 0 0 1
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 1105 0 0 0 0 0
## 1155 0 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPadmini3#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 33 10120 N iPadmini3#0 0 0
## 62 10177 Y iPadmini3#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 33 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 33 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 33 0 0 0
## 62 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr descr.my D.T.condit D.T.mint
## 33 10120 N iPadmini3#0 0 0
## 33.1 10120 N iPadmini3#0 0 0
## D.T.for. D.T.read D.T.wifi D.T.full D.T.usb D.T.X32gb D.T.part
## 33 0 0 0 0 0 0 0
## 33.1 0 0 0 0 0 0 0
## D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini D.P.air D.P.black
## 33 0 0 0 0 0 0 0
## 33.1 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 33 0 0 0
## 33.1 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini3#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 93 10227 Y iPadmini3#1
## 405 10691 N iPadmini3#1
## descr.my
## 93 Used for only two days mint!! W blue cover
## 405 Brandnew, never been used. Plastic has not been removed from iPad, however the box is not sealed in
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 93 0 0.8562937 0.6977759 0 0 0 0
## 405 0 0.0000000 0.0000000 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 93 0 0 0 0 0 0 0
## 405 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 93 0 0 0 0 0
## 405 0 0 0 0 0
## [1] "min distance(0.9673) pair:"
## UniqueID sold.fctr prdl.descr.my.fctr
## 606 11121 N iPadmini3#1
## 1111 12348 <NA> iPadmini3#1
## descr.my
## 606 Bought for one day and removed iPad plastic cover, never used. In original box with all accessories!
## 1111 Perfect working condition. See photos for actual item.
## D.T.condit D.T.mint D.T.for. D.T.read D.T.wifi D.T.full D.T.usb
## 606 0.0000000 0 0.3256287 0 0 0 0
## 1111 0.4184429 0 0.6977759 0 0 0 0
## D.T.X32gb D.T.part D.T.unlock D.T.adapt D.T.at D.P.http D.P.mini
## 606 0 0 0 0 0 0 0
## 1111 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 606 0 0 0 0 0
## 1111 0 0 0 0 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## Source: local data frame [27 x 5]
##
## prdl.descr.my.fctr.clusterid N Y .entropy .knt
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 Unknown#0_1 20 32 0.6662784 52
## 2 Unknown#1_1 7 22 0.5526646 29
## 3 Unknown#1_2 1 2 0.6365142 3
## 4 iPad1#0_1 12 55 0.4700328 67
## 5 iPad1#1_1 10 41 0.4949177 51
## 6 iPad2#0_1 16 67 0.4902162 83
## 7 iPad2#1_1 6 43 0.3717762 49
## 8 iPad2#1_2 3 5 0.6615632 8
## 9 iPad3#0_1 9 43 0.4607317 52
## 10 iPad3#1_1 5 20 0.5004024 25
## .. ... ... ... ... ...
## [1] "glb_allobs_df$prdl.descr.my.fctr$.clusterid Entropy: 0.5257 (98.8008 pct)"# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn
## 7 cluster.data 3 2 2 133.547
## 8 partition.data.training 4 0 0 169.201
## end elapsed
## 7 169.201 35.654
## 8 NA NAStep 4.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow = 1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
# Adjust OOB_strata_size (desired # of OOB obs) if > # of trn obs
ix <- which(!is.na(trnobs_univ_df$.n) & (OOB_strata_size > trnobs_univ_df$.n))
OOB_strata_size[ix] <- trnobs_univ_df[ix, ".n"]
require(sampling)
split_strata <- sampling::strata(tmp_trnobs_df,
stratanames = c(glb_rsp_var_raw, glb_category_var),
size = OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method = "srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## clusterif (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
if (!is.null(glb_obsfit_outliers)) {
glb_OOBobs_df <- rbind(glb_OOBobs_df,
glb_fitobs_df[glb_fitobs_df[, glb_id_var] %in% glb_obsfit_outliers, ])
glb_fitobs_df <- glb_fitobs_df[!(glb_fitobs_df[, glb_id_var] %in% glb_obsfit_outliers), ]
}
glb_allobs_df$.lcn <- ""; glb_trnobs_df$.lcn <- "";
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## sold.0 sold.1 sold.NA
## NA NA 376
## Fit 91 312 NA
## OOB 106 326 NA
## sold.0 sold.1 sold.NA
## NA NA 1
## Fit 0.2258065 0.7741935 NA
## OOB 0.2453704 0.7546296 NA
## prdl.descr.my.fctr .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 5 iPad2#0 52 58 0.138297872 0.134259259
## 15 iPadmini#0 39 44 0.103723404 0.101851852
## 3 iPad1#0 30 34 0.079787234 0.078703704
## 2 Unknown#1 24 28 0.063829787 0.064814815
## 11 iPadAir#0 22 25 0.058510638 0.057870370
## 17 iPadmini2#0 21 24 0.055851064 0.055555556
## 16 iPadmini#1 20 24 0.053191489 0.055555556
## 4 iPad1#1 20 23 0.053191489 0.053240741
## 13 iPadAir2#0 19 21 0.050531915 0.048611111
## 6 iPad2#1 18 21 0.047872340 0.048611111
## 7 iPad3#0 18 21 0.047872340 0.048611111
## 10 iPad4#1 15 17 0.039893617 0.039351852
## 1 Unknown#0 14 16 0.037234043 0.037037037
## 12 iPadAir#1 14 16 0.037234043 0.037037037
## 18 iPadmini2#1 13 15 0.034574468 0.034722222
## 9 iPad4#0 11 13 0.029255319 0.030092593
## 19 iPadmini3#0 11 13 0.029255319 0.030092593
## 14 iPadAir2#1 7 8 0.018617021 0.018518519
## 8 iPad3#1 5 7 0.013297872 0.016203704
## 20 iPadmini3#1 3 4 0.007978723 0.009259259print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 1211 102print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 835 102print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 403 101print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 432 101print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 376 101# # Does not handle NULL or length(glb_id_var) > 1
if (glb_save_envir)
save(glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor label_minor bgn
## 8 partition.data.training 4 0 0 169.201
## 9 select.features 5 0 0 170.030
## end elapsed
## 8 170.029 0.829
## 9 NA NAStep 5.0: select features
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glbFeatsExclude,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## sold sold 1.0000000000
## sprice.predict.diff sprice.predict.diff 0.5904093410
## spdiff.cut.fctr spdiff.cut.fctr 0.4823147264
## sprice.root2 sprice.root2 -0.4692227899
## startprice startprice -0.4639872150
## sprice.log10 sprice.log10 -0.3772958690
## sprice.d20nexp sprice.d20nexp 0.3243319036
## UniqueID UniqueID -0.1977676993
## startprice.log10.predict startprice.log10.predict -0.1910613631
## startprice.dcm2.is9 startprice.dcm2.is9 0.1368907902
## D.T.full D.T.full -0.1209351376
## startprice.dgt1.is9 startprice.dgt1.is9 -0.1107503988
## .clusterid .clusterid -0.0899696569
## .clusterid.fctr .clusterid.fctr -0.0899696569
## condition.fctr condition.fctr -0.0831080495
## startprice.dcm1.is9 startprice.dcm1.is9 0.0708630437
## D.T.at D.T.at -0.0701840185
## cellular.fctr cellular.fctr -0.0691186466
## D.T.X32gb D.T.X32gb -0.0655618349
## D.T.adapt D.T.adapt -0.0601455485
## D.T.wifi D.T.wifi -0.0576668264
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log -0.0523074591
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio -0.0517267931
## color.fctr color.fctr -0.0505379298
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 0.0498477578
## D.wrds.stop.n.log D.wrds.stop.n.log 0.0447603253
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -0.0440405256
## storage.fctr storage.fctr -0.0433987349
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 0.0433902126
## startprice.dgt2.is9 startprice.dgt2.is9 -0.0433551036
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -0.0431513736
## prdl.descr.my.fctr prdl.descr.my.fctr -0.0431252295
## carrier.fctr carrier.fctr -0.0428419431
## D.P.mini D.P.mini -0.0425451580
## D.P.black D.P.black -0.0417186983
## D.T.part D.T.part -0.0416125116
## .rnorm .rnorm -0.0412365211
## D.T.read D.T.read -0.0395079373
## D.T.mint D.T.mint -0.0395063880
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log 0.0376400859
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -0.0334543263
## D.T.unlock D.T.unlock 0.0333673516
## D.T.for. D.T.for. -0.0329826176
## D.dgts.n.log D.dgts.n.log -0.0323102108
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log 0.0286254240
## D.weight.post.stop.sum D.weight.post.stop.sum 0.0244386322
## D.T.usb D.T.usb 0.0236410532
## D.chrs.uppr.n.log D.chrs.uppr.n.log 0.0227376731
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log 0.0224893099
## D.chrs.n.log D.chrs.n.log 0.0223926673
## D.weight.post.stem.sum D.weight.post.stem.sum 0.0218009734
## D.weight.sum D.weight.sum 0.0218009734
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log 0.0192415365
## D.wrds.n.log D.wrds.n.log 0.0180346209
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n -0.0153588943
## D.P.spacegray D.P.spacegray -0.0137760301
## D.terms.post.stem.n.log D.terms.post.stem.n.log 0.0124040836
## D.wrds.unq.n.log D.wrds.unq.n.log 0.0124040836
## D.terms.post.stop.n.log D.terms.post.stop.n.log 0.0123794904
## D.P.white D.P.white 0.0120080147
## D.terms.post.stop.n D.terms.post.stop.n -0.0076401230
## D.terms.post.stem.n D.terms.post.stem.n -0.0074148023
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 0.0065678581
## D.P.air D.P.air -0.0032597677
## D.T.condit D.T.condit 0.0018761918
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log 0.0016299746
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio -0.0012589533
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -0.0007877674
## biddable biddable NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## D.P.http D.P.http NA
## D.P.gold D.P.gold NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs
## sold 1 1.0000000000
## sprice.predict.diff 1 0.5904093410
## spdiff.cut.fctr 0 0.4823147264
## sprice.root2 0 0.4692227899
## startprice 1 0.4639872150
## sprice.log10 0 0.3772958690
## sprice.d20nexp 0 0.3243319036
## UniqueID 1 0.1977676993
## startprice.log10.predict 1 0.1910613631
## startprice.dcm2.is9 0 0.1368907902
## D.T.full 0 0.1209351376
## startprice.dgt1.is9 0 0.1107503988
## .clusterid 1 0.0899696569
## .clusterid.fctr 0 0.0899696569
## condition.fctr 0 0.0831080495
## startprice.dcm1.is9 0 0.0708630437
## D.T.at 0 0.0701840185
## cellular.fctr 0 0.0691186466
## D.T.X32gb 0 0.0655618349
## D.T.adapt 0 0.0601455485
## D.T.wifi 0 0.0576668264
## D.chrs.pnct16.n.log 0 0.0523074591
## D.weight.sum.stem.stop.Ratio 0 0.0517267931
## color.fctr 0 0.0505379298
## D.chrs.pnct08.n.log 0 0.0498477578
## D.wrds.stop.n.log 0 0.0447603253
## D.chrs.pnct14.n.log 0 0.0440405256
## storage.fctr 0 0.0433987349
## D.chrs.pnct01.n.log 0 0.0433902126
## startprice.dgt2.is9 0 0.0433551036
## D.chrs.pnct05.n.log 0 0.0431513736
## prdl.descr.my.fctr 0 0.0431252295
## carrier.fctr 0 0.0428419431
## D.P.mini 0 0.0425451580
## D.P.black 0 0.0417186983
## D.T.part 0 0.0416125116
## .rnorm 0 0.0412365211
## D.T.read 0 0.0395079373
## D.T.mint 0 0.0395063880
## D.chrs.pnct06.n.log 0 0.0376400859
## D.chrs.pnct12.n.log 0 0.0334543263
## D.T.unlock 0 0.0333673516
## D.T.for. 0 0.0329826176
## D.dgts.n.log 0 0.0323102108
## D.chrs.pnct11.n.log 0 0.0286254240
## D.weight.post.stop.sum 0 0.0244386322
## D.T.usb 0 0.0236410532
## D.chrs.uppr.n.log 0 0.0227376731
## D.chrs.pnct13.n.log 0 0.0224893099
## D.chrs.n.log 0 0.0223926673
## D.weight.post.stem.sum 0 0.0218009734
## D.weight.sum 0 0.0218009734
## D.chrs.pnct17.n.log 0 0.0192415365
## D.wrds.n.log 0 0.0180346209
## D.ratio.wrds.stop.n.wrds.n 0 0.0153588943
## D.P.spacegray 0 0.0137760301
## D.terms.post.stem.n.log 0 0.0124040836
## D.wrds.unq.n.log 0 0.0124040836
## D.terms.post.stop.n.log 0 0.0123794904
## D.P.white 0 0.0120080147
## D.terms.post.stop.n 1 0.0076401230
## D.terms.post.stem.n 1 0.0074148023
## D.chrs.pnct15.n.log 0 0.0065678581
## D.P.air 0 0.0032597677
## D.T.condit 0 0.0018761918
## D.chrs.pnct07.n.log 0 0.0016299746
## D.terms.n.stem.stop.Ratio 0 0.0012589533
## D.ratio.weight.sum.wrds.n 0 0.0007877674
## biddable 0 NA
## D.chrs.pnct02.n.log 0 NA
## D.chrs.pnct03.n.log 0 NA
## D.chrs.pnct04.n.log 0 NA
## D.chrs.pnct09.n.log 0 NA
## D.chrs.pnct10.n.log 0 NA
## D.chrs.pnct18.n.log 0 NA
## D.chrs.pnct19.n.log 0 NA
## D.chrs.pnct20.n.log 0 NA
## D.chrs.pnct21.n.log 0 NA
## D.chrs.pnct22.n.log 0 NA
## D.chrs.pnct23.n.log 0 NA
## D.chrs.pnct24.n.log 0 NA
## D.chrs.pnct25.n.log 0 NA
## D.chrs.pnct26.n.log 0 NA
## D.chrs.pnct27.n.log 0 NA
## D.chrs.pnct28.n.log 0 NA
## D.chrs.pnct29.n.log 0 NA
## D.chrs.pnct30.n.log 0 NA
## D.P.http 0 NA
## D.P.gold 0 NA
## startprice.dgt3.is9 0 NAprint(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df, rsp_var=glb_rsp_var,
nzv.freqCut=glb_nzv_freqCut, nzv.uniqueCut=glb_nzv_uniqueCut)))## [1] "cor(sprice.d20nexp, sprice.log10)=-0.9450"
## [1] "cor(sold.fctr, sprice.d20nexp)=0.3243"
## [1] "cor(sold.fctr, sprice.log10)=-0.3773"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified sprice.d20nexp as highly correlated with
## sprice.log10## [1] "cor(startprice.dcm1.is9, startprice.dcm2.is9)=0.8837"
## [1] "cor(sold.fctr, startprice.dcm1.is9)=0.0709"
## [1] "cor(sold.fctr, startprice.dcm2.is9)=0.1369"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified startprice.dcm1.is9 as highly correlated with
## startprice.dcm2.is9## [1] "cor(sprice.log10, sprice.root2)=0.8784"
## [1] "cor(sold.fctr, sprice.log10)=-0.3773"
## [1] "cor(sold.fctr, sprice.root2)=-0.4692"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified sprice.log10 as highly correlated with
## sprice.root2## [1] "cor(carrier.fctr, cellular.fctr)=0.7265"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0428"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0691"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## [1] "cor(D.T.full, D.T.wifi)=0.7130"
## [1] "cor(sold.fctr, D.T.full)=-0.1209"
## [1] "cor(sold.fctr, D.T.wifi)=-0.0577"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.T.wifi as highly correlated with D.T.full## id cor.y
## sold sold 1.0000000000
## sprice.predict.diff sprice.predict.diff 0.5904093410
## spdiff.cut.fctr spdiff.cut.fctr 0.4823147264
## sprice.d20nexp sprice.d20nexp 0.3243319036
## startprice.dcm2.is9 startprice.dcm2.is9 0.1368907902
## startprice.dcm1.is9 startprice.dcm1.is9 0.0708630437
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 0.0498477578
## D.wrds.stop.n.log D.wrds.stop.n.log 0.0447603253
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 0.0433902126
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log 0.0376400859
## D.T.unlock D.T.unlock 0.0333673516
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log 0.0286254240
## D.weight.post.stop.sum D.weight.post.stop.sum 0.0244386322
## D.T.usb D.T.usb 0.0236410532
## D.chrs.uppr.n.log D.chrs.uppr.n.log 0.0227376731
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log 0.0224893099
## D.chrs.n.log D.chrs.n.log 0.0223926673
## D.weight.post.stem.sum D.weight.post.stem.sum 0.0218009734
## D.weight.sum D.weight.sum 0.0218009734
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log 0.0192415365
## D.wrds.n.log D.wrds.n.log 0.0180346209
## D.terms.post.stem.n.log D.terms.post.stem.n.log 0.0124040836
## D.wrds.unq.n.log D.wrds.unq.n.log 0.0124040836
## D.terms.post.stop.n.log D.terms.post.stop.n.log 0.0123794904
## D.P.white D.P.white 0.0120080147
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 0.0065678581
## D.T.condit D.T.condit 0.0018761918
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log 0.0016299746
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -0.0007877674
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio -0.0012589533
## D.P.air D.P.air -0.0032597677
## D.terms.post.stem.n D.terms.post.stem.n -0.0074148023
## D.terms.post.stop.n D.terms.post.stop.n -0.0076401230
## D.P.spacegray D.P.spacegray -0.0137760301
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n -0.0153588943
## D.dgts.n.log D.dgts.n.log -0.0323102108
## D.T.for. D.T.for. -0.0329826176
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -0.0334543263
## D.T.mint D.T.mint -0.0395063880
## D.T.read D.T.read -0.0395079373
## .rnorm .rnorm -0.0412365211
## D.T.part D.T.part -0.0416125116
## D.P.black D.P.black -0.0417186983
## D.P.mini D.P.mini -0.0425451580
## carrier.fctr carrier.fctr -0.0428419431
## prdl.descr.my.fctr prdl.descr.my.fctr -0.0431252295
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -0.0431513736
## startprice.dgt2.is9 startprice.dgt2.is9 -0.0433551036
## storage.fctr storage.fctr -0.0433987349
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -0.0440405256
## color.fctr color.fctr -0.0505379298
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio -0.0517267931
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log -0.0523074591
## D.T.wifi D.T.wifi -0.0576668264
## D.T.adapt D.T.adapt -0.0601455485
## D.T.X32gb D.T.X32gb -0.0655618349
## cellular.fctr cellular.fctr -0.0691186466
## D.T.at D.T.at -0.0701840185
## condition.fctr condition.fctr -0.0831080495
## .clusterid .clusterid -0.0899696569
## .clusterid.fctr .clusterid.fctr -0.0899696569
## startprice.dgt1.is9 startprice.dgt1.is9 -0.1107503988
## D.T.full D.T.full -0.1209351376
## startprice.log10.predict startprice.log10.predict -0.1910613631
## UniqueID UniqueID -0.1977676993
## sprice.log10 sprice.log10 -0.3772958690
## startprice startprice -0.4639872150
## sprice.root2 sprice.root2 -0.4692227899
## D.P.gold D.P.gold NA
## D.P.http D.P.http NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## biddable biddable NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs
## sold 1 1.0000000000
## sprice.predict.diff 1 0.5904093410
## spdiff.cut.fctr 0 0.4823147264
## sprice.d20nexp 0 0.3243319036
## startprice.dcm2.is9 0 0.1368907902
## startprice.dcm1.is9 0 0.0708630437
## D.chrs.pnct08.n.log 0 0.0498477578
## D.wrds.stop.n.log 0 0.0447603253
## D.chrs.pnct01.n.log 0 0.0433902126
## D.chrs.pnct06.n.log 0 0.0376400859
## D.T.unlock 0 0.0333673516
## D.chrs.pnct11.n.log 0 0.0286254240
## D.weight.post.stop.sum 0 0.0244386322
## D.T.usb 0 0.0236410532
## D.chrs.uppr.n.log 0 0.0227376731
## D.chrs.pnct13.n.log 0 0.0224893099
## D.chrs.n.log 0 0.0223926673
## D.weight.post.stem.sum 0 0.0218009734
## D.weight.sum 0 0.0218009734
## D.chrs.pnct17.n.log 0 0.0192415365
## D.wrds.n.log 0 0.0180346209
## D.terms.post.stem.n.log 0 0.0124040836
## D.wrds.unq.n.log 0 0.0124040836
## D.terms.post.stop.n.log 0 0.0123794904
## D.P.white 0 0.0120080147
## D.chrs.pnct15.n.log 0 0.0065678581
## D.T.condit 0 0.0018761918
## D.chrs.pnct07.n.log 0 0.0016299746
## D.ratio.weight.sum.wrds.n 0 0.0007877674
## D.terms.n.stem.stop.Ratio 0 0.0012589533
## D.P.air 0 0.0032597677
## D.terms.post.stem.n 1 0.0074148023
## D.terms.post.stop.n 1 0.0076401230
## D.P.spacegray 0 0.0137760301
## D.ratio.wrds.stop.n.wrds.n 0 0.0153588943
## D.dgts.n.log 0 0.0323102108
## D.T.for. 0 0.0329826176
## D.chrs.pnct12.n.log 0 0.0334543263
## D.T.mint 0 0.0395063880
## D.T.read 0 0.0395079373
## .rnorm 0 0.0412365211
## D.T.part 0 0.0416125116
## D.P.black 0 0.0417186983
## D.P.mini 0 0.0425451580
## carrier.fctr 0 0.0428419431
## prdl.descr.my.fctr 0 0.0431252295
## D.chrs.pnct05.n.log 0 0.0431513736
## startprice.dgt2.is9 0 0.0433551036
## storage.fctr 0 0.0433987349
## D.chrs.pnct14.n.log 0 0.0440405256
## color.fctr 0 0.0505379298
## D.weight.sum.stem.stop.Ratio 0 0.0517267931
## D.chrs.pnct16.n.log 0 0.0523074591
## D.T.wifi 0 0.0576668264
## D.T.adapt 0 0.0601455485
## D.T.X32gb 0 0.0655618349
## cellular.fctr 0 0.0691186466
## D.T.at 0 0.0701840185
## condition.fctr 0 0.0831080495
## .clusterid 1 0.0899696569
## .clusterid.fctr 0 0.0899696569
## startprice.dgt1.is9 0 0.1107503988
## D.T.full 0 0.1209351376
## startprice.log10.predict 1 0.1910613631
## UniqueID 1 0.1977676993
## sprice.log10 0 0.3772958690
## startprice 1 0.4639872150
## sprice.root2 0 0.4692227899
## D.P.gold 0 NA
## D.P.http 0 NA
## D.chrs.pnct02.n.log 0 NA
## D.chrs.pnct03.n.log 0 NA
## D.chrs.pnct04.n.log 0 NA
## D.chrs.pnct09.n.log 0 NA
## D.chrs.pnct10.n.log 0 NA
## D.chrs.pnct18.n.log 0 NA
## D.chrs.pnct19.n.log 0 NA
## D.chrs.pnct20.n.log 0 NA
## D.chrs.pnct21.n.log 0 NA
## D.chrs.pnct22.n.log 0 NA
## D.chrs.pnct23.n.log 0 NA
## D.chrs.pnct24.n.log 0 NA
## D.chrs.pnct25.n.log 0 NA
## D.chrs.pnct26.n.log 0 NA
## D.chrs.pnct27.n.log 0 NA
## D.chrs.pnct28.n.log 0 NA
## D.chrs.pnct29.n.log 0 NA
## D.chrs.pnct30.n.log 0 NA
## biddable 0 NA
## startprice.dgt3.is9 0 NA
## cor.high.X freqRatio percentUnique
## sold <NA> 3.238579 0.2395210
## sprice.predict.diff <NA> 1.000000 91.3772455
## spdiff.cut.fctr <NA> 1.305556 0.9580838
## sprice.d20nexp sprice.log10 3.560976 24.6706587
## startprice.dcm2.is9 <NA> 2.092593 0.2395210
## startprice.dcm1.is9 startprice.dcm2.is9 1.693548 0.2395210
## D.chrs.pnct08.n.log <NA> 91.444444 0.3592814
## D.wrds.stop.n.log <NA> 9.417910 1.1976048
## D.chrs.pnct01.n.log <NA> 44.833333 0.4790419
## D.chrs.pnct06.n.log <NA> 277.000000 0.3592814
## D.T.unlock <NA> 277.333333 0.2395210
## D.chrs.pnct11.n.log <NA> 8.925926 0.7185629
## D.weight.post.stop.sum <NA> 86.166667 34.0119760
## D.T.usb <NA> 414.500000 0.7185629
## D.chrs.uppr.n.log <NA> 21.500000 8.9820359
## D.chrs.pnct13.n.log <NA> 6.196078 0.9580838
## D.chrs.n.log <NA> 16.645161 9.8203593
## D.weight.post.stem.sum <NA> 86.166667 34.0119760
## D.weight.sum <NA> 86.166667 34.0119760
## D.chrs.pnct17.n.log <NA> 834.000000 0.2395210
## D.wrds.n.log <NA> 14.333333 2.8742515
## D.terms.post.stem.n.log <NA> 15.666667 2.3952096
## D.wrds.unq.n.log <NA> 15.666667 2.3952096
## D.terms.post.stop.n.log <NA> 14.361111 2.3952096
## D.P.white <NA> 207.500000 0.3592814
## D.chrs.pnct15.n.log <NA> 166.000000 0.2395210
## D.T.condit <NA> 66.545455 2.6347305
## D.chrs.pnct07.n.log <NA> 74.545455 0.3592814
## D.ratio.weight.sum.wrds.n <NA> 86.166667 34.0119760
## D.terms.n.stem.stop.Ratio <NA> 116.000000 1.1976048
## D.P.air <NA> 103.375000 0.2395210
## D.terms.post.stem.n <NA> 15.666667 2.3952096
## D.terms.post.stop.n <NA> 14.361111 2.3952096
## D.P.spacegray <NA> 277.333333 0.2395210
## D.ratio.wrds.stop.n.wrds.n <NA> 18.464286 7.7844311
## D.dgts.n.log <NA> 66.500000 1.0778443
## D.T.for. <NA> 161.800000 2.0359281
## D.chrs.pnct12.n.log <NA> 27.586207 0.3592814
## D.T.mint <NA> 411.000000 1.3173653
## D.T.read <NA> 273.333333 1.1976048
## .rnorm <NA> 1.000000 100.0000000
## D.T.part <NA> 416.000000 0.3592814
## D.P.black <NA> 118.285714 0.2395210
## D.P.mini <NA> 82.500000 0.2395210
## carrier.fctr cellular.fctr 3.736111 0.8383234
## prdl.descr.my.fctr <NA> 1.012195 2.3952096
## D.chrs.pnct05.n.log <NA> 207.750000 0.2395210
## startprice.dgt2.is9 <NA> 8.277778 0.2395210
## storage.fctr <NA> 2.786164 0.5988024
## D.chrs.pnct14.n.log <NA> 63.230769 0.2395210
## color.fctr <NA> 1.595000 0.5988024
## D.weight.sum.stem.stop.Ratio <NA> 89.166667 32.2155689
## D.chrs.pnct16.n.log <NA> 277.000000 0.3592814
## D.T.wifi D.T.full 274.666667 0.9580838
## D.T.adapt <NA> 415.000000 0.5988024
## D.T.X32gb <NA> 414.500000 0.7185629
## cellular.fctr <NA> 2.599034 0.3592814
## D.T.at <NA> 831.000000 0.5988024
## condition.fctr <NA> 5.424528 0.7185629
## .clusterid <NA> 24.303030 0.2395210
## .clusterid.fctr <NA> 24.303030 0.2395210
## startprice.dgt1.is9 <NA> 3.093137 0.2395210
## D.T.full <NA> 276.333333 0.5988024
## startprice.log10.predict <NA> 1.230769 73.2934132
## UniqueID <NA> 1.000000 100.0000000
## sprice.log10 sprice.root2 3.560976 24.6706587
## startprice <NA> 3.560976 24.6706587
## sprice.root2 <NA> 3.560976 24.6706587
## D.P.gold <NA> 0.000000 0.1197605
## D.P.http <NA> 0.000000 0.1197605
## D.chrs.pnct02.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct03.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct04.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct09.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct10.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct18.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct19.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct20.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct21.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct22.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct23.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct24.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct25.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct26.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct27.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct28.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct29.n.log <NA> 0.000000 0.1197605
## D.chrs.pnct30.n.log <NA> 0.000000 0.1197605
## biddable <NA> 0.000000 0.1197605
## startprice.dgt3.is9 <NA> 0.000000 0.1197605
## zeroVar nzv is.cor.y.abs.low
## sold FALSE FALSE FALSE
## sprice.predict.diff FALSE FALSE FALSE
## spdiff.cut.fctr FALSE FALSE FALSE
## sprice.d20nexp FALSE FALSE FALSE
## startprice.dcm2.is9 FALSE FALSE FALSE
## startprice.dcm1.is9 FALSE FALSE FALSE
## D.chrs.pnct08.n.log FALSE TRUE FALSE
## D.wrds.stop.n.log FALSE FALSE FALSE
## D.chrs.pnct01.n.log FALSE TRUE FALSE
## D.chrs.pnct06.n.log FALSE TRUE TRUE
## D.T.unlock FALSE TRUE TRUE
## D.chrs.pnct11.n.log FALSE FALSE TRUE
## D.weight.post.stop.sum FALSE FALSE TRUE
## D.T.usb FALSE TRUE TRUE
## D.chrs.uppr.n.log FALSE TRUE TRUE
## D.chrs.pnct13.n.log FALSE FALSE TRUE
## D.chrs.n.log FALSE FALSE TRUE
## D.weight.post.stem.sum FALSE FALSE TRUE
## D.weight.sum FALSE FALSE TRUE
## D.chrs.pnct17.n.log FALSE TRUE TRUE
## D.wrds.n.log FALSE FALSE TRUE
## D.terms.post.stem.n.log FALSE FALSE TRUE
## D.wrds.unq.n.log FALSE FALSE TRUE
## D.terms.post.stop.n.log FALSE FALSE TRUE
## D.P.white FALSE TRUE TRUE
## D.chrs.pnct15.n.log FALSE TRUE TRUE
## D.T.condit FALSE TRUE TRUE
## D.chrs.pnct07.n.log FALSE TRUE TRUE
## D.ratio.weight.sum.wrds.n FALSE FALSE TRUE
## D.terms.n.stem.stop.Ratio FALSE TRUE TRUE
## D.P.air FALSE TRUE TRUE
## D.terms.post.stem.n FALSE FALSE TRUE
## D.terms.post.stop.n FALSE FALSE TRUE
## D.P.spacegray FALSE TRUE TRUE
## D.ratio.wrds.stop.n.wrds.n FALSE FALSE TRUE
## D.dgts.n.log FALSE TRUE TRUE
## D.T.for. FALSE TRUE TRUE
## D.chrs.pnct12.n.log FALSE TRUE TRUE
## D.T.mint FALSE TRUE TRUE
## D.T.read FALSE TRUE TRUE
## .rnorm FALSE FALSE FALSE
## D.T.part FALSE TRUE FALSE
## D.P.black FALSE TRUE FALSE
## D.P.mini FALSE TRUE FALSE
## carrier.fctr FALSE FALSE FALSE
## prdl.descr.my.fctr FALSE FALSE FALSE
## D.chrs.pnct05.n.log FALSE TRUE FALSE
## startprice.dgt2.is9 FALSE FALSE FALSE
## storage.fctr FALSE FALSE FALSE
## D.chrs.pnct14.n.log FALSE TRUE FALSE
## color.fctr FALSE FALSE FALSE
## D.weight.sum.stem.stop.Ratio FALSE FALSE FALSE
## D.chrs.pnct16.n.log FALSE TRUE FALSE
## D.T.wifi FALSE TRUE FALSE
## D.T.adapt FALSE TRUE FALSE
## D.T.X32gb FALSE TRUE FALSE
## cellular.fctr FALSE FALSE FALSE
## D.T.at FALSE TRUE FALSE
## condition.fctr FALSE FALSE FALSE
## .clusterid FALSE TRUE FALSE
## .clusterid.fctr FALSE TRUE FALSE
## startprice.dgt1.is9 FALSE FALSE FALSE
## D.T.full FALSE TRUE FALSE
## startprice.log10.predict FALSE FALSE FALSE
## UniqueID FALSE FALSE FALSE
## sprice.log10 FALSE FALSE FALSE
## startprice FALSE FALSE FALSE
## sprice.root2 FALSE FALSE FALSE
## D.P.gold TRUE TRUE NA
## D.P.http TRUE TRUE NA
## D.chrs.pnct02.n.log TRUE TRUE NA
## D.chrs.pnct03.n.log TRUE TRUE NA
## D.chrs.pnct04.n.log TRUE TRUE NA
## D.chrs.pnct09.n.log TRUE TRUE NA
## D.chrs.pnct10.n.log TRUE TRUE NA
## D.chrs.pnct18.n.log TRUE TRUE NA
## D.chrs.pnct19.n.log TRUE TRUE NA
## D.chrs.pnct20.n.log TRUE TRUE NA
## D.chrs.pnct21.n.log TRUE TRUE NA
## D.chrs.pnct22.n.log TRUE TRUE NA
## D.chrs.pnct23.n.log TRUE TRUE NA
## D.chrs.pnct24.n.log TRUE TRUE NA
## D.chrs.pnct25.n.log TRUE TRUE NA
## D.chrs.pnct26.n.log TRUE TRUE NA
## D.chrs.pnct27.n.log TRUE TRUE NA
## D.chrs.pnct28.n.log TRUE TRUE NA
## D.chrs.pnct29.n.log TRUE TRUE NA
## D.chrs.pnct30.n.log TRUE TRUE NA
## biddable TRUE TRUE NA
## startprice.dgt3.is9 TRUE TRUE NAplt_feats_df <- glb_feats_df
print(myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
colorcol_name="nzv", jitter=TRUE) +
#geom_point(aes(shape=nzv)) +
geom_point() +
xlim(-5, 25) +
geom_hline(yintercept=glb_nzv_freqCut) +
geom_vline(xintercept=glb_nzv_uniqueCut))## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).
print(subset(glb_feats_df, nzv))## id cor.y
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 0.049847758
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 0.043390213
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log 0.037640086
## D.T.unlock D.T.unlock 0.033367352
## D.T.usb D.T.usb 0.023641053
## D.chrs.uppr.n.log D.chrs.uppr.n.log 0.022737673
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log 0.019241536
## D.P.white D.P.white 0.012008015
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 0.006567858
## D.T.condit D.T.condit 0.001876192
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log 0.001629975
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio -0.001258953
## D.P.air D.P.air -0.003259768
## D.P.spacegray D.P.spacegray -0.013776030
## D.dgts.n.log D.dgts.n.log -0.032310211
## D.T.for. D.T.for. -0.032982618
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -0.033454326
## D.T.mint D.T.mint -0.039506388
## D.T.read D.T.read -0.039507937
## D.T.part D.T.part -0.041612512
## D.P.black D.P.black -0.041718698
## D.P.mini D.P.mini -0.042545158
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -0.043151374
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -0.044040526
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log -0.052307459
## D.T.wifi D.T.wifi -0.057666826
## D.T.adapt D.T.adapt -0.060145549
## D.T.X32gb D.T.X32gb -0.065561835
## D.T.at D.T.at -0.070184018
## .clusterid .clusterid -0.089969657
## .clusterid.fctr .clusterid.fctr -0.089969657
## D.T.full D.T.full -0.120935138
## D.P.gold D.P.gold NA
## D.P.http D.P.http NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## biddable biddable NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs cor.high.X freqRatio
## D.chrs.pnct08.n.log 0 0.049847758 <NA> 91.44444
## D.chrs.pnct01.n.log 0 0.043390213 <NA> 44.83333
## D.chrs.pnct06.n.log 0 0.037640086 <NA> 277.00000
## D.T.unlock 0 0.033367352 <NA> 277.33333
## D.T.usb 0 0.023641053 <NA> 414.50000
## D.chrs.uppr.n.log 0 0.022737673 <NA> 21.50000
## D.chrs.pnct17.n.log 0 0.019241536 <NA> 834.00000
## D.P.white 0 0.012008015 <NA> 207.50000
## D.chrs.pnct15.n.log 0 0.006567858 <NA> 166.00000
## D.T.condit 0 0.001876192 <NA> 66.54545
## D.chrs.pnct07.n.log 0 0.001629975 <NA> 74.54545
## D.terms.n.stem.stop.Ratio 0 0.001258953 <NA> 116.00000
## D.P.air 0 0.003259768 <NA> 103.37500
## D.P.spacegray 0 0.013776030 <NA> 277.33333
## D.dgts.n.log 0 0.032310211 <NA> 66.50000
## D.T.for. 0 0.032982618 <NA> 161.80000
## D.chrs.pnct12.n.log 0 0.033454326 <NA> 27.58621
## D.T.mint 0 0.039506388 <NA> 411.00000
## D.T.read 0 0.039507937 <NA> 273.33333
## D.T.part 0 0.041612512 <NA> 416.00000
## D.P.black 0 0.041718698 <NA> 118.28571
## D.P.mini 0 0.042545158 <NA> 82.50000
## D.chrs.pnct05.n.log 0 0.043151374 <NA> 207.75000
## D.chrs.pnct14.n.log 0 0.044040526 <NA> 63.23077
## D.chrs.pnct16.n.log 0 0.052307459 <NA> 277.00000
## D.T.wifi 0 0.057666826 D.T.full 274.66667
## D.T.adapt 0 0.060145549 <NA> 415.00000
## D.T.X32gb 0 0.065561835 <NA> 414.50000
## D.T.at 0 0.070184018 <NA> 831.00000
## .clusterid 1 0.089969657 <NA> 24.30303
## .clusterid.fctr 0 0.089969657 <NA> 24.30303
## D.T.full 0 0.120935138 <NA> 276.33333
## D.P.gold 0 NA <NA> 0.00000
## D.P.http 0 NA <NA> 0.00000
## D.chrs.pnct02.n.log 0 NA <NA> 0.00000
## D.chrs.pnct03.n.log 0 NA <NA> 0.00000
## D.chrs.pnct04.n.log 0 NA <NA> 0.00000
## D.chrs.pnct09.n.log 0 NA <NA> 0.00000
## D.chrs.pnct10.n.log 0 NA <NA> 0.00000
## D.chrs.pnct18.n.log 0 NA <NA> 0.00000
## D.chrs.pnct19.n.log 0 NA <NA> 0.00000
## D.chrs.pnct20.n.log 0 NA <NA> 0.00000
## D.chrs.pnct21.n.log 0 NA <NA> 0.00000
## D.chrs.pnct22.n.log 0 NA <NA> 0.00000
## D.chrs.pnct23.n.log 0 NA <NA> 0.00000
## D.chrs.pnct24.n.log 0 NA <NA> 0.00000
## D.chrs.pnct25.n.log 0 NA <NA> 0.00000
## D.chrs.pnct26.n.log 0 NA <NA> 0.00000
## D.chrs.pnct27.n.log 0 NA <NA> 0.00000
## D.chrs.pnct28.n.log 0 NA <NA> 0.00000
## D.chrs.pnct29.n.log 0 NA <NA> 0.00000
## D.chrs.pnct30.n.log 0 NA <NA> 0.00000
## biddable 0 NA <NA> 0.00000
## startprice.dgt3.is9 0 NA <NA> 0.00000
## percentUnique zeroVar nzv is.cor.y.abs.low
## D.chrs.pnct08.n.log 0.3592814 FALSE TRUE FALSE
## D.chrs.pnct01.n.log 0.4790419 FALSE TRUE FALSE
## D.chrs.pnct06.n.log 0.3592814 FALSE TRUE TRUE
## D.T.unlock 0.2395210 FALSE TRUE TRUE
## D.T.usb 0.7185629 FALSE TRUE TRUE
## D.chrs.uppr.n.log 8.9820359 FALSE TRUE TRUE
## D.chrs.pnct17.n.log 0.2395210 FALSE TRUE TRUE
## D.P.white 0.3592814 FALSE TRUE TRUE
## D.chrs.pnct15.n.log 0.2395210 FALSE TRUE TRUE
## D.T.condit 2.6347305 FALSE TRUE TRUE
## D.chrs.pnct07.n.log 0.3592814 FALSE TRUE TRUE
## D.terms.n.stem.stop.Ratio 1.1976048 FALSE TRUE TRUE
## D.P.air 0.2395210 FALSE TRUE TRUE
## D.P.spacegray 0.2395210 FALSE TRUE TRUE
## D.dgts.n.log 1.0778443 FALSE TRUE TRUE
## D.T.for. 2.0359281 FALSE TRUE TRUE
## D.chrs.pnct12.n.log 0.3592814 FALSE TRUE TRUE
## D.T.mint 1.3173653 FALSE TRUE TRUE
## D.T.read 1.1976048 FALSE TRUE TRUE
## D.T.part 0.3592814 FALSE TRUE FALSE
## D.P.black 0.2395210 FALSE TRUE FALSE
## D.P.mini 0.2395210 FALSE TRUE FALSE
## D.chrs.pnct05.n.log 0.2395210 FALSE TRUE FALSE
## D.chrs.pnct14.n.log 0.2395210 FALSE TRUE FALSE
## D.chrs.pnct16.n.log 0.3592814 FALSE TRUE FALSE
## D.T.wifi 0.9580838 FALSE TRUE FALSE
## D.T.adapt 0.5988024 FALSE TRUE FALSE
## D.T.X32gb 0.7185629 FALSE TRUE FALSE
## D.T.at 0.5988024 FALSE TRUE FALSE
## .clusterid 0.2395210 FALSE TRUE FALSE
## .clusterid.fctr 0.2395210 FALSE TRUE FALSE
## D.T.full 0.5988024 FALSE TRUE FALSE
## D.P.gold 0.1197605 TRUE TRUE NA
## D.P.http 0.1197605 TRUE TRUE NA
## D.chrs.pnct02.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct03.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct04.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct09.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct10.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct18.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct19.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct20.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct21.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct22.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct23.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct24.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct25.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct26.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct27.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct28.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct29.n.log 0.1197605 TRUE TRUE NA
## D.chrs.pnct30.n.log 0.1197605 TRUE TRUE NA
## biddable 0.1197605 TRUE TRUE NA
## startprice.dgt3.is9 0.1197605 TRUE TRUE NAtmp_allobs_df <-
glb_allobs_df[, union(setdiff(names(glb_allobs_df), subset(glb_feats_df, nzv)$id),
glb_cluster_entropy_var)]
glb_trnobs_df <- subset(tmp_allobs_df, .src == "Train")
glb_newobs_df <- subset(tmp_allobs_df, .src == "Test")
glb_feats_df$interaction.feat <- NA
for (feat in names(glb_interaction_only_feats_lst))
glb_feats_df[glb_feats_df$id %in% feat, "interaction.feat"] <-
glb_interaction_only_feats_lst[[feat]]
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
numeric_indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]
glb_feats_df$shapiro.test.p.value <- NA
glb_feats_df[glb_feats_df$id %in% numeric_indep_vars, "shapiro.test.p.value"] <-
sapply(numeric_indep_vars, function(var) shapiro.test(glb_trnobs_df[, var])$p.value)
not_nrml_feats_df <- glb_feats_df %>%
subset(!is.na(shapiro.test.p.value)) %>%
subset((shapiro.test.p.value < 0.05) || (id == ".rnorm")) %>%
arrange(shapiro.test.p.value)
row.names(not_nrml_feats_df) <- not_nrml_feats_df$id
#plt_trnobs_df <- glb_trnobs_df[, c("D.npnct05.log", ".rnorm")]
plt_trnobs_df <- glb_trnobs_df[, c(union(not_nrml_feats_df$id[1:min(5, nrow(not_nrml_feats_df))],
".rnorm"), glb_cluster_entropy_var)]
print(myplot_violin(plt_trnobs_df, setdiff(names(plt_trnobs_df), glb_cluster_entropy_var),
xcol_name = glb_cluster_entropy_var) +
facet_wrap(~variable, scales="free"))
#myplot_histogram(plt_trnobs_df, "D.npnct11.log", fill_col_name="sold", show_stats = TRUE)
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
# shd .clusterid.fctr be excluded from this ? or include encoding of glb_category_var:.clusterid.fctr ?
indep_vars <- myadjust_interaction_feats(subset(glb_feats_df,
!nzv & (exclude.as.feat != 1))$id)
myrun_rfe <- function(obs_df, indep_vars, sizes=NULL) {
rfe_obs_df <- myget_vectorized_obs_df(obs_df, glb_rsp_var, indep_vars)
predictors_vctr <- setdiff(names(rfe_obs_df), glb_rsp_var)
if (is.null(sizes))
sizes <- tail(2 ^ (1:as.integer(log2(length(predictors_vctr)))), 5)
rfe_control <- rfeControl(functions = rfFuncs, method = "repeatedcv",
number = glb_rcv_n_folds, repeats = glb_rcv_n_repeats,
verbose = TRUE, returnResamp = "all",
seeds = mygen_seeds(seeds_lst_len =
(glb_rcv_n_folds * glb_rcv_n_repeats) + 1,
seeds_elmnt_lst_len = (length(sizes) + 1)))
set.seed(113)
rfe_results <- rfe(rfe_obs_df[, predictors_vctr],
rfe_obs_df[, glb_rsp_var],
sizes = sizes,
# metric = unlist(strsplit(glbMdlMetricsEval, "[.]"))[2],
# maximize = ifelse(unlist(strsplit(glbMdlMetricsEval, "[.]"))[1] == "max",
# TRUE, FALSE),
rfeControl = rfe_control)
print(rfe_results)
print(predictors(rfe_results))
# print(plot(rfe_results, type=c("g", "o")))
# print(plot(rfe_results))
print(ggplot(rfe_results))
return(rfe_results)
}
rfe_fit_results <- myrun_rfe(glb_fitobs_df, indep_vars, glb_rfe_fit_sizes)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 47 0.8288 0.4661 0.02962 0.08551
## 48 0.8288 0.4695 0.03402 0.09605
## 49 0.8296 0.4686 0.02596 0.07919
## 50 0.8329 0.4793 0.02822 0.08114
## 51 0.8280 0.4671 0.02425 0.06693
## 52 0.8280 0.4622 0.02645 0.07878
## 53 0.8313 0.4709 0.02526 0.07615
## 54 0.8288 0.4660 0.03088 0.08859
## 82 0.8371 0.4939 0.02884 0.07747 *
##
## The top 5 variables (out of 82):
## spdiff.cut.fctr(-1e+03,-100], sprice.d20nexp, sprice.root2, sprice.log10, spdiff.cut.fctr(10,100]
##
## [1] "spdiff.cut.fctr(-1e+03,-100]"
## [2] "sprice.d20nexp"
## [3] "sprice.root2"
## [4] "sprice.log10"
## [5] "spdiff.cut.fctr(10,100]"
## [6] "cellular.fctrUnknown:carrier.fctrUnknown"
## [7] "startprice.dgt1.is9"
## [8] "cellular.fctrUnknown"
## [9] "condition.fctrFor parts or not working"
## [10] "D.weight.sum.stem.stop.Ratio"
## [11] "D.wrds.unq.n.log"
## [12] "D.terms.post.stem.n.log"
## [13] "prdl.descr.my.fctriPadAir2#1"
## [14] "spdiff.cut.fctr(-100,-10]"
## [15] "prdl.descr.my.fctriPad3#0"
## [16] "D.terms.post.stop.n.log"
## [17] "startprice.dcm1.is9"
## [18] "spdiff.cut.fctr(-1,0]"
## [19] "prdl.descr.my.fctriPadAir#0"
## [20] "prdl.descr.my.fctriPad4#1"
## [21] "D.ratio.wrds.stop.n.wrds.n"
## [22] "D.weight.post.stop.sum"
## [23] "prdl.descr.my.fctriPad2#0"
## [24] "prdl.descr.my.fctriPadmini#0"
## [25] "D.weight.sum"
## [26] "startprice.dcm2.is9"
## [27] "D.weight.post.stem.sum"
## [28] "spdiff.cut.fctr(1,10]"
## [29] "prdl.descr.my.fctriPadmini#1"
## [30] "startprice.dgt2.is9"
## [31] "D.wrds.n.log"
## [32] "D.chrs.pnct11.n.log"
## [33] "prdl.descr.my.fctriPad1#1"
## [34] "D.chrs.n.log"
## [35] "cellular.fctr0:carrier.fctrNone"
## [36] "prdl.descr.my.fctriPad4#0"
## [37] "spdiff.cut.fctr(0,1]"
## [38] "prdl.descr.my.fctriPad1#0"
## [39] "color.fctrWhite"
## [40] "spdiff.cut.fctr(100,1e+03]"
## [41] "spdiff.cut.fctr(-10,-1]"
## [42] "condition.fctrSeller refurbished"
## [43] "cellular.fctr1"
## [44] "condition.fctrNew"
## [45] "prdl.descr.my.fctriPadAir#1"
## [46] "D.wrds.stop.n.log"
## [47] "color.fctrUnknown"
## [48] "D.ratio.weight.sum.wrds.n"
## [49] "D.chrs.pnct13.n.log"
## [50] "storage.fctr64"
## [51] "prdl.descr.my.fctriPad2#1"
## [52] "cellular.fctr1:carrier.fctrUnknown"
## [53] "prdl.descr.my.fctriPadmini3#0"
## [54] "color.fctrSpace Gray"
## [55] "storage.fctrUnknown"
## [56] "storage.fctr32"
## [57] "prdl.descr.my.fctriPad3#1"
## [58] "condition.fctrManufacturer refurbished"
## [59] "cellular.fctr1:carrier.fctrVerizon"
## [60] ".rnorm"
## [61] "prdl.descr.my.fctrUnknown#1"
## [62] "condition.fctrNew other (see details)"
## [63] "cellular.fctr0:carrier.fctrOther"
## [64] "cellular.fctr0:carrier.fctrSprint"
## [65] "cellular.fctr0:carrier.fctrT-Mobile"
## [66] "cellular.fctr0:carrier.fctrUnknown"
## [67] "cellular.fctr0:carrier.fctrVerizon"
## [68] "cellular.fctr1:carrier.fctrNone"
## [69] "cellular.fctr1:carrier.fctrOther"
## [70] "cellular.fctr1:carrier.fctrSprint"
## [71] "cellular.fctrUnknown:carrier.fctrNone"
## [72] "cellular.fctrUnknown:carrier.fctrOther"
## [73] "cellular.fctrUnknown:carrier.fctrSprint"
## [74] "cellular.fctrUnknown:carrier.fctrT-Mobile"
## [75] "cellular.fctrUnknown:carrier.fctrVerizon"
## [76] "prdl.descr.my.fctriPadmini3#1"
## [77] "prdl.descr.my.fctriPadmini2#0"
## [78] "prdl.descr.my.fctriPadmini2#1"
## [79] "prdl.descr.my.fctriPadAir2#0"
## [80] "cellular.fctr1:carrier.fctrT-Mobile"
## [81] "storage.fctr16"
## [82] "color.fctrGold"
# print(all.equal(rfe_results[-which(names(rfe_results) == "times")],
# sav_rfe_results[-which(names(sav_rfe_results) == "times")]))
# require(mRMRe)
# indep_vars_vctr <- subset(glb_feats_df, !nzv &
# (exclude.as.feat != 1))[, "id"]
# indep_vars_vctr <- setdiff(indep_vars_vctr,
# myfind_fctr_cols_df(glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]))
# tmp_trnobs_df <- glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]
# tmp_trnobs_df$biddable <- as.numeric(tmp_trnobs_df$biddable)
# dd <- mRMR.data(data = tmp_trnobs_df)
# mRMRe.fltr <- mRMR.classic(data = dd, target_indices = c(1), feature_count = 10)
# print(solutions(mRMRe.fltr)[[1]])
# print(apply(solutions(mRMRe.fltr)[[1]], 2, function(x, y) { return(y[x]) },
# y=featureNames(dd)))
# print(featureNames(dd)[solutions(mRMRe.fltr)[[1]]])
# print(mRMRe.fltr@filters); print(mRMRe.fltr@scores)
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 376 376
## [1] "numeric data w/ 0s in : "
## sold sprice.log10
## 197 31
## cellular.fctr D.terms.post.stop.n
## 789 754
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 754 754
## D.terms.post.stem.n D.terms.post.stem.n.log
## 754 754
## D.weight.post.stem.sum D.T.condit
## 754 1052
## D.T.mint D.T.for.
## 1192 1170
## D.T.read D.T.wifi
## 1194 1196
## D.T.full D.T.usb
## 1202 1204
## D.T.X32gb D.T.part
## 1203 1206
## D.T.unlock D.T.adapt
## 1207 1206
## D.T.at D.wrds.n.log
## 1207 753
## D.wrds.unq.n.log D.weight.sum
## 754 754
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 754 753
## D.chrs.uppr.n.log D.dgts.n.log
## 753 1159
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 1173 1211
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 1211 1211
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 1204 1206
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 1191 1194
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 1210 1211
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 1060 1165
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 923 1191
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 1205 1207
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 1210 1211
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 1211 1211
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 1211 1211
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 1211 1211
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 1211 1211
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 1211 1211
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 1211 1211
## D.wrds.stop.n.log D.P.http
## 923 1211
## D.P.mini D.P.air
## 1197 1202
## D.P.black D.P.white
## 1199 1206
## D.P.gold D.P.spacegray
## 1211 1207
## startprice.dgt1.is9 startprice.dgt2.is9
## 908 1064
## startprice.dgt3.is9 startprice.dcm1.is9
## 1211 775
## startprice.dcm2.is9
## 830
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid descr.my .lcn
## 0 NA 753 376# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 90 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## sold.fctr sold.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## sold sold 1.0000000 TRUE 1.0000000 <NA>
## UniqueID UniqueID -0.1977677 TRUE 0.1977677 <NA>
## sold.fctr sold.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## sold 3.238579 0.239521 FALSE FALSE FALSE
## UniqueID 1.000000 100.000000 FALSE FALSE FALSE
## sold.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var rsp_var
## sold <NA> NA TRUE NA NA
## UniqueID <NA> NA FALSE TRUE NA
## sold.fctr <NA> NA NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## character(0)print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "selfts_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 9 select.features 5 0 0 170.030 188.519
## 10 fit.models 6 0 0 188.519 NA
## elapsed
## 9 18.489
## 10 NAStep 6.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glb_out_pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
c("id.prefix", "method", "type",
# trainControl params
"preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# train params
"metric", "metric.maximize", "tune.df")## [1] "id.prefix" "method" "type"
## [4] "preProc.method" "cv.n.folds" "cv.n.repeats"
## [7] "summary.fn" "metric" "metric.maximize"
## [10] "tune.df"# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"## Warning in if (mdl_specs_lst[["train.method"]] %in% c("bayesglm", "glm", :
## the condition has length > 1 and only the first element will be used## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## Y N
## 0.7741935 0.2258065
## [1] "MFO.val:"
## [1] "Y"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:wordcloud':
##
## textplot
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.7741935 0.2258065
## 2 0.7741935 0.2258065
## 3 0.7741935 0.2258065
## 4 0.7741935 0.2258065
## 5 0.7741935 0.2258065
## 6 0.7741935 0.2258065
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.Y
## 1 N 91
## 2 Y 312
## Prediction
## Reference N Y
## N 0 91
## Y 0 312
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.741935e-01 0.000000e+00 7.301947e-01 8.141127e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 5.280696e-01 3.926255e-21
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.7741935 0.2258065
## 2 0.7741935 0.2258065
## 3 0.7741935 0.2258065
## 4 0.7741935 0.2258065
## 5 0.7741935 0.2258065
## 6 0.7741935 0.2258065
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.Y
## 1 N 106
## 2 Y 326
## Prediction
## Reference N Y
## N 0 106
## Y 0 326
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.546296e-01 0.000000e+00 7.112313e-01 7.945009e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 5.260575e-01 2.013550e-24
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.myMFO_classfr .rnorm 0 0.293
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.003 0.5 0 1
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.2 0.8727273 0.7741935
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7301947 0.8141127 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 0 1 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.8601583 0.7546296
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7112313 0.7945009 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"## Warning in if (mdl_specs_lst[["train.method"]] %in% c("bayesglm", "glm", :
## the condition has length > 1 and only the first element will be used## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "in Random.Classifier$prob"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 91
## 2 Y 312
## Prediction
## Reference N Y
## N 0 91
## Y 0 312
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.741935e-01 0.000000e+00 7.301947e-01 8.141127e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 5.280696e-01 3.926255e-21
## [1] "in Random.Classifier$prob"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 106
## 2 Y 326
## Prediction
## Reference N Y
## N 0 106
## Y 0 326
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.546296e-01 0.000000e+00 7.112313e-01 7.945009e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 5.260575e-01 2.013550e-24
## id feats max.nTuningRuns
## 1 Random.myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.274 0.001 0.5054945
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.2417582 0.7692308 0.5112179 0.2
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.8727273 0.7741935 0.7301947
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8141127 0 0.5212988 0.2358491
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.8067485 0.53108 0.2 0.8601583
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7546296 0.7112313 0.7945009
## max.Kappa.OOB
## 1 0# ret_lst <- myfit_mdl(mdl_id = "Random", model_method = "myrandom_classfr",
# model_type = glb_model_type,
# indep_vars_vctr = ".rnorm",
# rsp_var = glb_rsp_var, rsp_var_out = glb_rsp_var_out,
# fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except bag & rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rcv.1X1.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"## Loading required package: glmnet
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loaded glmnet 2.0-2## Fitting alpha = 0.1, lambda = 0.00423 on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 1.3045755 0.9427025
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 1.7791310 3.3225778
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 3.1334079 2.8112171
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.5942968 4.7362098
## sprice.root2
## -0.1265541
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 1.2668649 0.9668979
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 1.8106939 3.4233987
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 3.2352811 2.8657020
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.6275732 4.8286022
## sprice.root2
## -0.1253197
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.N
## 1 N 46
## 2 Y 15
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.Y
## 1 45
## 2 297
## Prediction
## Reference N Y
## N 46 45
## Y 15 297
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.511166e-01 5.178849e-01 8.125512e-01 8.844240e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 7.384632e-05 1.811974e-04
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.N
## 1 N 55
## 2 Y 8
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.Y
## 1 51
## 2 318
## Prediction
## Reference N Y
## N 55 51
## Y 8 318
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.634259e-01 5.727218e-01 8.273915e-01 8.943819e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 1.673557e-08 4.553107e-08
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1.glmnet spdiff.cut.fctr,sprice.root2 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.762 0.03 0.753663
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5714286 0.9358974 0.8966434 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.9082569 0.8511166 0.8125512
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.884424 0.5178849 0.747164 0.5188679
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.9754601 0.8730032 0.5 0.9151079
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.8634259 0.8273915 0.8943819
## max.Kappa.OOB
## 1 0.5727218# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds, trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
}## [1] "fitting model: Max.cor.Y.rcv.3X1.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000911 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8441854 1.2181523
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1400769 4.9534292
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.7772971 3.4510453
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9607768 6.2329383
## sprice.root2
## -0.1099784
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8287020 1.2267835
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1514437 5.0427690
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.8667913 3.4716512
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9718351 6.3173344
## sprice.root2
## -0.1093736
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.N
## 1 N 49
## 2 Y 16
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.Y
## 1 42
## 2 296
## Prediction
## Reference N Y
## N 49 42
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.560794e-01 5.420275e-01 8.179561e-01 8.888671e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.460262e-05 1.028323e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X1.glmnet spdiff.cut.fctr,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.408 0.019 0.7481685
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5604396 0.9358974 0.8976648 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.9107692 0.8561083 0.8179561
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8888671 0.5565229 0.7486978 0.5188679
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.9785276 0.8747106 0.5 0.9166667
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.8657407 0.8299202 0.8964488
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5784089 0.01076837 0.04682919
## [1] "fitting model: Max.cor.Y.rcv.3X3.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000911 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8441854 1.2181523
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1400769 4.9534292
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.7772971 3.4510453
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9607768 6.2329383
## sprice.root2
## -0.1099784
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8287020 1.2267835
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1514437 5.0427690
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.8667913 3.4716512
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9718351 6.3173344
## sprice.root2
## -0.1093736
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.N
## 1 N 49
## 2 Y 16
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.Y
## 1 42
## 2 296
## Prediction
## Reference N Y
## N 49 42
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.560794e-01 5.420275e-01 8.179561e-01 8.888671e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.460262e-05 1.028323e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X3.glmnet spdiff.cut.fctr,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.021 0.02 0.7481685
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5604396 0.9358974 0.8976648 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.9107692 0.8528223 0.8179561
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8888671 0.5488242 0.7486978 0.5188679
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.9785276 0.8747106 0.5 0.9166667
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.8657407 0.8299202 0.8964488
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5784089 0.02087114 0.05769317
## [1] "fitting model: Max.cor.Y.rcv.3X5.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000196 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.6925704 1.3012274
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.2494509 6.3221326
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 6.1426025 3.6493372
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 3.0658584 7.5631093
## sprice.root2
## -0.1039489
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "spdiff.cut.fctr(-100,-10]"
## [3] "spdiff.cut.fctr(-10,-1]" "spdiff.cut.fctr(-1,0]"
## [5] "spdiff.cut.fctr(0,1]" "spdiff.cut.fctr(1,10]"
## [7] "spdiff.cut.fctr(10,100]" "spdiff.cut.fctr(100,1e+03]"
## [9] "sprice.root2"
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.N
## 1 N 50
## 2 Y 16
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.Y
## 1 41
## 2 296
## Prediction
## Reference N Y
## N 50 41
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.585608e-01 5.518641e-01 8.206637e-01 8.910835e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 1.379763e-05 1.478465e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X5.glmnet spdiff.cut.fctr,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.559 0.021 0.7481685
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5604396 0.9358974 0.8979466 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.9121726 0.8531306 0.8206637
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8910835 0.5466137 0.7486978 0.5188679
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.9785276 0.8749132 0.5 0.9166667
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.8657407 0.8299202 0.8964488
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5784089 0.02507525 0.07638378
## [1] "fitting model: Max.cor.Y.rcv.5X1.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00423 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 1.3045755 0.9427025
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 1.7791310 3.3225778
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 3.1334079 2.8112171
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.5942968 4.7362098
## sprice.root2
## -0.1265541
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 1.2668649 0.9668979
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 1.8106939 3.4233987
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 3.2352811 2.8657020
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.6275732 4.8286022
## sprice.root2
## -0.1253197
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.N
## 1 N 46
## 2 Y 15
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.Y
## 1 45
## 2 297
## Prediction
## Reference N Y
## N 46 45
## Y 15 297
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.511166e-01 5.178849e-01 8.125512e-01 8.844240e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 7.384632e-05 1.811974e-04
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.N
## 1 N 55
## 2 Y 8
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.Y
## 1 51
## 2 318
## Prediction
## Reference N Y
## N 55 51
## Y 8 318
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.634259e-01 5.727218e-01 8.273915e-01 8.943819e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 1.673557e-08 4.553107e-08
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X1.glmnet spdiff.cut.fctr,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.638 0.019 0.753663
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5714286 0.9358974 0.8966434 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.9082569 0.8510426 0.8125512
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.884424 0.5425612 0.747164 0.5188679
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.9754601 0.8730032 0.5 0.9151079
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.8634259 0.8273915 0.8943819
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5727218 0.0413584 0.134876
## [1] "fitting model: Max.cor.Y.rcv.5X3.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000911 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8441854 1.2181523
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1400769 4.9534292
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.7772971 3.4510453
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9607768 6.2329383
## sprice.root2
## -0.1099784
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8287020 1.2267835
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1514437 5.0427690
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.8667913 3.4716512
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9718351 6.3173344
## sprice.root2
## -0.1093736
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.N
## 1 N 49
## 2 Y 16
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.Y
## 1 42
## 2 296
## Prediction
## Reference N Y
## N 49 42
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.560794e-01 5.420275e-01 8.179561e-01 8.888671e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.460262e-05 1.028323e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X3.glmnet spdiff.cut.fctr,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.396 0.019 0.7481685
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5604396 0.9358974 0.8976648 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.9107692 0.8536089 0.8179561
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8888671 0.5444976 0.7486978 0.5188679
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.9785276 0.8747106 0.5 0.9166667
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.8657407 0.8299202 0.8964488
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5784089 0.03207616 0.1108485
## [1] "fitting model: Max.cor.Y.rcv.5X5.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.000196 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 96 -none- numeric
## beta 768 dgCMatrix S4
## df 96 -none- numeric
## dim 2 -none- numeric
## lambda 96 -none- numeric
## dev.ratio 96 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.6828156 1.3049271
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.2540222 6.6070839
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 6.2931247 3.6578073
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 3.0704878 7.4687048
## sprice.root2
## -0.1034602
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.6788218 1.3072038
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.2570278 6.6919260
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 6.3787085 3.6631572
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 3.0732852 7.5570100
## sprice.root2
## -0.1033060
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.N
## 1 N 50
## 2 Y 16
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.Y
## 1 41
## 2 296
## Prediction
## Reference N Y
## N 50 41
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.585608e-01 5.518641e-01 8.206637e-01 8.910835e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 1.379763e-05 1.478465e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X5.glmnet spdiff.cut.fctr,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 3.706 0.022 0.7481685
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5604396 0.9358974 0.8979114 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.9121726 0.8541198 0.8206637
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8910835 0.5454265 0.7486978 0.5188679
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.9785276 0.8747974 0.5 0.9166667
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.8657407 0.8299202 0.8964488
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5784089 0.03026295 0.1047386# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1.cp.0", type=glb_model_type, trainControl.method="none",
train.method="rpart",
tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rcv.1X1.cp.0.rpart"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"## Loading required package: rpart## Fitting cp = 0 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 403
##
## CP nsplit rel error
## 1 0.131868132 0 1.0000000
## 2 0.032967033 2 0.7362637
## 3 0.014652015 3 0.7032967
## 4 0.010989011 6 0.6593407
## 5 0.003663004 8 0.6373626
## 6 0.000000000 11 0.6263736
##
## Variable importance
## sprice.root2 spdiff.cut.fctr(10,100]
## 72 18
## spdiff.cut.fctr(-100,-10] spdiff.cut.fctr(100,1e+03]
## 9 1
##
## Node number 1: 403 observations, complexity param=0.1318681
## predicted class=Y expected loss=0.2258065 P(node) =1
## class counts: 91 312
## probabilities: 0.226 0.774
## left son=2 (136 obs) right son=3 (267 obs)
## Primary splits:
## sprice.root2 < 11.93667 to the right, improve=34.265420, (0 missing)
## spdiff.cut.fctr(-1,0] < 0.5 to the left, improve= 6.632332, (0 missing)
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve= 4.667453, (0 missing)
## spdiff.cut.fctr(1,10] < 0.5 to the left, improve= 3.826191, (0 missing)
## spdiff.cut.fctr(0,1] < 0.5 to the left, improve= 2.373042, (0 missing)
## Surrogate splits:
## spdiff.cut.fctr(100,1e+03] < 0.5 to the right, agree=0.667, adj=0.015, (0 split)
##
## Node number 2: 136 observations, complexity param=0.1318681
## predicted class=N expected loss=0.4852941 P(node) =0.337469
## class counts: 70 66
## probabilities: 0.515 0.485
## left son=4 (112 obs) right son=5 (24 obs)
## Primary splits:
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve=10.8459400, (0 missing)
## sprice.root2 < 20.54751 to the right, improve= 3.0013160, (0 missing)
## spdiff.cut.fctr(-100,-10] < 0.5 to the left, improve= 0.3091384, (0 missing)
##
## Node number 3: 267 observations, complexity param=0.01465201
## predicted class=Y expected loss=0.07865169 P(node) =0.662531
## class counts: 21 246
## probabilities: 0.079 0.921
## left son=6 (56 obs) right son=7 (211 obs)
## Primary splits:
## spdiff.cut.fctr(-100,-10] < 0.5 to the right, improve=2.6072590, (0 missing)
## sprice.root2 < 8.887737 to the right, improve=2.5016990, (0 missing)
## spdiff.cut.fctr(-1,0] < 0.5 to the left, improve=0.8570066, (0 missing)
## spdiff.cut.fctr(0,1] < 0.5 to the left, improve=0.2966292, (0 missing)
## spdiff.cut.fctr(1,10] < 0.5 to the left, improve=0.2852327, (0 missing)
##
## Node number 4: 112 observations, complexity param=0.03296703
## predicted class=N expected loss=0.3928571 P(node) =0.2779156
## class counts: 68 44
## probabilities: 0.607 0.393
## left son=8 (73 obs) right son=9 (39 obs)
## Primary splits:
## spdiff.cut.fctr(-100,-10] < 0.5 to the left, improve=2.537107, (0 missing)
## sprice.root2 < 20.54751 to the right, improve=1.782260, (0 missing)
## Surrogate splits:
## sprice.root2 < 12.92213 to the right, agree=0.714, adj=0.179, (0 split)
##
## Node number 5: 24 observations
## predicted class=Y expected loss=0.08333333 P(node) =0.05955335
## class counts: 2 22
## probabilities: 0.083 0.917
##
## Node number 6: 56 observations, complexity param=0.01465201
## predicted class=Y expected loss=0.2142857 P(node) =0.1389578
## class counts: 12 44
## probabilities: 0.214 0.786
## left son=12 (33 obs) right son=13 (23 obs)
## Primary splits:
## sprice.root2 < 8.887737 to the right, improve=1.265575, (0 missing)
##
## Node number 7: 211 observations
## predicted class=Y expected loss=0.04265403 P(node) =0.5235732
## class counts: 9 202
## probabilities: 0.043 0.957
##
## Node number 8: 73 observations, complexity param=0.003663004
## predicted class=N expected loss=0.3150685 P(node) =0.1811414
## class counts: 50 23
## probabilities: 0.685 0.315
## left son=16 (21 obs) right son=17 (52 obs)
## Primary splits:
## sprice.root2 < 20.54751 to the right, improve=0.9152742, (0 missing)
## Surrogate splits:
## spdiff.cut.fctr(100,1e+03] < 0.5 to the right, agree=0.726, adj=0.048, (0 split)
##
## Node number 9: 39 observations, complexity param=0.01098901
## predicted class=Y expected loss=0.4615385 P(node) =0.09677419
## class counts: 18 21
## probabilities: 0.462 0.538
## left son=18 (27 obs) right son=19 (12 obs)
## Primary splits:
## sprice.root2 < 15.89005 to the left, improve=0.5698006, (0 missing)
##
## Node number 12: 33 observations, complexity param=0.01465201
## predicted class=Y expected loss=0.3030303 P(node) =0.08188586
## class counts: 10 23
## probabilities: 0.303 0.697
## left son=24 (8 obs) right son=25 (25 obs)
## Primary splits:
## sprice.root2 < 9.810502 to the left, improve=4.219394, (0 missing)
##
## Node number 13: 23 observations
## predicted class=Y expected loss=0.08695652 P(node) =0.05707196
## class counts: 2 21
## probabilities: 0.087 0.913
##
## Node number 16: 21 observations
## predicted class=N expected loss=0.1904762 P(node) =0.05210918
## class counts: 17 4
## probabilities: 0.810 0.190
##
## Node number 17: 52 observations, complexity param=0.003663004
## predicted class=N expected loss=0.3653846 P(node) =0.1290323
## class counts: 33 19
## probabilities: 0.635 0.365
## left son=34 (25 obs) right son=35 (27 obs)
## Primary splits:
## sprice.root2 < 15.79556 to the left, improve=0.7020513, (0 missing)
##
## Node number 18: 27 observations, complexity param=0.01098901
## predicted class=N expected loss=0.4814815 P(node) =0.06699752
## class counts: 14 13
## probabilities: 0.519 0.481
## left son=36 (12 obs) right son=37 (15 obs)
## Primary splits:
## sprice.root2 < 13.69276 to the right, improve=0.1814815, (0 missing)
##
## Node number 19: 12 observations
## predicted class=Y expected loss=0.3333333 P(node) =0.02977667
## class counts: 4 8
## probabilities: 0.333 0.667
##
## Node number 24: 8 observations
## predicted class=N expected loss=0.25 P(node) =0.01985112
## class counts: 6 2
## probabilities: 0.750 0.250
##
## Node number 25: 25 observations
## predicted class=Y expected loss=0.16 P(node) =0.06203474
## class counts: 4 21
## probabilities: 0.160 0.840
##
## Node number 34: 25 observations
## predicted class=N expected loss=0.28 P(node) =0.06203474
## class counts: 18 7
## probabilities: 0.720 0.280
##
## Node number 35: 27 observations, complexity param=0.003663004
## predicted class=N expected loss=0.4444444 P(node) =0.06699752
## class counts: 15 12
## probabilities: 0.556 0.444
## left son=70 (10 obs) right son=71 (17 obs)
## Primary splits:
## sprice.root2 < 18.21985 to the right, improve=0.6627451, (0 missing)
##
## Node number 36: 12 observations
## predicted class=N expected loss=0.4166667 P(node) =0.02977667
## class counts: 7 5
## probabilities: 0.583 0.417
##
## Node number 37: 15 observations
## predicted class=Y expected loss=0.4666667 P(node) =0.03722084
## class counts: 7 8
## probabilities: 0.467 0.533
##
## Node number 70: 10 observations
## predicted class=N expected loss=0.3 P(node) =0.0248139
## class counts: 7 3
## probabilities: 0.700 0.300
##
## Node number 71: 17 observations
## predicted class=Y expected loss=0.4705882 P(node) =0.04218362
## class counts: 8 9
## probabilities: 0.471 0.529
##
## n= 403
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 403 91 Y (0.22580645 0.77419355)
## 2) sprice.root2>=11.93667 136 66 N (0.51470588 0.48529412)
## 4) spdiff.cut.fctr(10,100]< 0.5 112 44 N (0.60714286 0.39285714)
## 8) spdiff.cut.fctr(-100,-10]< 0.5 73 23 N (0.68493151 0.31506849)
## 16) sprice.root2>=20.54751 21 4 N (0.80952381 0.19047619) *
## 17) sprice.root2< 20.54751 52 19 N (0.63461538 0.36538462)
## 34) sprice.root2< 15.79556 25 7 N (0.72000000 0.28000000) *
## 35) sprice.root2>=15.79556 27 12 N (0.55555556 0.44444444)
## 70) sprice.root2>=18.21985 10 3 N (0.70000000 0.30000000) *
## 71) sprice.root2< 18.21985 17 8 Y (0.47058824 0.52941176) *
## 9) spdiff.cut.fctr(-100,-10]>=0.5 39 18 Y (0.46153846 0.53846154)
## 18) sprice.root2< 15.89005 27 13 N (0.51851852 0.48148148)
## 36) sprice.root2>=13.69276 12 5 N (0.58333333 0.41666667) *
## 37) sprice.root2< 13.69276 15 7 Y (0.46666667 0.53333333) *
## 19) sprice.root2>=15.89005 12 4 Y (0.33333333 0.66666667) *
## 5) spdiff.cut.fctr(10,100]>=0.5 24 2 Y (0.08333333 0.91666667) *
## 3) sprice.root2< 11.93667 267 21 Y (0.07865169 0.92134831)
## 6) spdiff.cut.fctr(-100,-10]>=0.5 56 12 Y (0.21428571 0.78571429)
## 12) sprice.root2>=8.887737 33 10 Y (0.30303030 0.69696970)
## 24) sprice.root2< 9.810502 8 2 N (0.75000000 0.25000000) *
## 25) sprice.root2>=9.810502 25 4 Y (0.16000000 0.84000000) *
## 13) sprice.root2< 8.887737 23 2 Y (0.08695652 0.91304348) *
## 7) spdiff.cut.fctr(-100,-10]< 0.5 211 9 Y (0.04265403 0.95734597) *
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.N
## 1 N 55
## 2 Y 21
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.Y
## 1 36
## 2 291
## Prediction
## Reference N Y
## N 55 36
## Y 21 291
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.585608e-01 5.703866e-01 8.206637e-01 8.910835e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 1.379763e-05 6.368978e-02
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.N
## 1 N 10
## 2 Y 1
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.Y
## 1 96
## 2 325
## Prediction
## Reference N Y
## N 10 96
## Y 1 325
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.754630e-01 1.308388e-01 7.331391e-01 8.139670e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 1.711709e-01 1.370907e-21
## id feats
## 1 Max.cor.Y.rcv.1X1.cp.0.rpart spdiff.cut.fctr,sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.731 0.013
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.768544 0.6043956 0.9326923 0.8748239
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9107981 0.8585608
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8206637 0.8910835 0.5703866
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6697824 0.4622642 0.8773006 0.8205087
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.8701473 0.775463
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7331391 0.813967 0.1308388#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="rpart")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## + Fold1.Rep1: cp=0.003663
## - Fold1.Rep1: cp=0.003663
## + Fold2.Rep1: cp=0.003663
## - Fold2.Rep1: cp=0.003663
## + Fold3.Rep1: cp=0.003663
## - Fold3.Rep1: cp=0.003663
## + Fold1.Rep2: cp=0.003663
## - Fold1.Rep2: cp=0.003663
## + Fold2.Rep2: cp=0.003663
## - Fold2.Rep2: cp=0.003663
## + Fold3.Rep2: cp=0.003663
## - Fold3.Rep2: cp=0.003663
## + Fold1.Rep3: cp=0.003663
## - Fold1.Rep3: cp=0.003663
## + Fold2.Rep3: cp=0.003663
## - Fold2.Rep3: cp=0.003663
## + Fold3.Rep3: cp=0.003663
## - Fold3.Rep3: cp=0.003663
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.033 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 403
##
## CP nsplit rel error
## 1 0.13186813 0 1.0000000
## 2 0.03296703 2 0.7362637
##
## Variable importance
## sprice.root2 spdiff.cut.fctr(10,100]
## 75 24
## spdiff.cut.fctr(100,1e+03]
## 1
##
## Node number 1: 403 observations, complexity param=0.1318681
## predicted class=Y expected loss=0.2258065 P(node) =1
## class counts: 91 312
## probabilities: 0.226 0.774
## left son=2 (136 obs) right son=3 (267 obs)
## Primary splits:
## sprice.root2 < 11.93667 to the right, improve=34.265420, (0 missing)
## spdiff.cut.fctr(-1,0] < 0.5 to the left, improve= 6.632332, (0 missing)
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve= 4.667453, (0 missing)
## spdiff.cut.fctr(1,10] < 0.5 to the left, improve= 3.826191, (0 missing)
## spdiff.cut.fctr(0,1] < 0.5 to the left, improve= 2.373042, (0 missing)
## Surrogate splits:
## spdiff.cut.fctr(100,1e+03] < 0.5 to the right, agree=0.667, adj=0.015, (0 split)
##
## Node number 2: 136 observations, complexity param=0.1318681
## predicted class=N expected loss=0.4852941 P(node) =0.337469
## class counts: 70 66
## probabilities: 0.515 0.485
## left son=4 (112 obs) right son=5 (24 obs)
## Primary splits:
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve=10.8459400, (0 missing)
## sprice.root2 < 20.54751 to the right, improve= 3.0013160, (0 missing)
## spdiff.cut.fctr(-100,-10] < 0.5 to the left, improve= 0.3091384, (0 missing)
##
## Node number 3: 267 observations
## predicted class=Y expected loss=0.07865169 P(node) =0.662531
## class counts: 21 246
## probabilities: 0.079 0.921
##
## Node number 4: 112 observations
## predicted class=N expected loss=0.3928571 P(node) =0.2779156
## class counts: 68 44
## probabilities: 0.607 0.393
##
## Node number 5: 24 observations
## predicted class=Y expected loss=0.08333333 P(node) =0.05955335
## class counts: 2 22
## probabilities: 0.083 0.917
##
## n= 403
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 403 91 Y (0.22580645 0.77419355)
## 2) sprice.root2>=11.93667 136 66 N (0.51470588 0.48529412)
## 4) spdiff.cut.fctr(10,100]< 0.5 112 44 N (0.60714286 0.39285714) *
## 5) spdiff.cut.fctr(10,100]>=0.5 24 2 Y (0.08333333 0.91666667) *
## 3) sprice.root2< 11.93667 267 21 Y (0.07865169 0.92134831) *
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 68
## 2 Y 44
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 23
## 2 268
## Prediction
## Reference N Y
## N 68 23
## Y 44 268
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.833746898 0.560423280 0.793733671 0.868771672 0.774193548
## AccuracyPValue McnemarPValue
## 0.001950422 0.014550049
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 N 106
## 2 Y 326
## Prediction
## Reference N Y
## N 0 106
## Y 0 326
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.546296e-01 0.000000e+00 7.112313e-01 7.945009e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 5.260575e-01 2.013550e-24
## id feats max.nTuningRuns
## 1 Max.cor.Y.rpart spdiff.cut.fctr,sprice.root2 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.503 0.012 0.8031136
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.7472527 0.8589744 0.8036419 0.9
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.8888889 0.8156317 0.7937337
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8687717 0.4861128 0.7600995 0.6981132
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.8220859 0.754254 0.3 0.8601583
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7546296 0.7112313 0.7945009
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0 0.0286103 0.1013095if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
} ## [1] "fitting model: Interact.High.cor.Y.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2,spdiff.cut.fctr:sprice.log10,spdiff.cut.fctr:startprice.dcm2.is9,spdiff.cut.fctr:cellular.fctr,spdiff.cut.fctr:sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0219 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 95 -none- numeric
## beta 4465 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 47 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 3.0768409439
## sprice.root2
## -0.0800106627
## spdiff.cut.fctr(-1e+03,-100]:cellular.fctr1
## -0.0009191992
## spdiff.cut.fctr(-1e+03,-100]:cellular.fctrUnknown
## -0.7696098057
## spdiff.cut.fctr(-100,-10]:cellular.fctrUnknown
## -0.7854860577
## spdiff.cut.fctr(-1e+03,-100]:sprice.log10
## -0.3996798082
## spdiff.cut.fctr(-100,-10]:sprice.log10
## -0.2201082313
## spdiff.cut.fctr(-1e+03,-100]:startprice.dcm2.is9
## -0.7929707094
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 3.12689350
## sprice.root2
## -0.08134481
## spdiff.cut.fctr(-1e+03,-100]:cellular.fctr1
## -0.07456820
## spdiff.cut.fctr(-1e+03,-100]:cellular.fctrUnknown
## -0.87591799
## spdiff.cut.fctr(-100,-10]:cellular.fctrUnknown
## -0.82973188
## spdiff.cut.fctr(-1e+03,-100]:sprice.log10
## -0.39876958
## spdiff.cut.fctr(-100,-10]:sprice.log10
## -0.22946624
## spdiff.cut.fctr(-1e+03,-100]:startprice.dcm2.is9
## -0.89862114
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glmnet.N
## 1 N 53
## 2 Y 17
## sold.fctr.predict.Interact.High.cor.Y.glmnet.Y
## 1 38
## 2 295
## Prediction
## Reference N Y
## N 53 38
## Y 17 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.635236e-01 5.749190e-01 8.260892e-01 8.955055e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 4.090242e-06 7.000942e-03
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glmnet.N
## 1 N 58
## 2 Y 12
## sold.fctr.predict.Interact.High.cor.Y.glmnet.Y
## 1 48
## 2 314
## Prediction
## Reference N Y
## N 58 48
## Y 12 314
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.611111e-01 5.764152e-01 8.248659e-01 8.923117e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 3.443416e-08 6.228499e-06
## id
## 1 Interact.High.cor.Y.glmnet
## feats
## 1 spdiff.cut.fctr,sprice.root2,spdiff.cut.fctr:sprice.log10,spdiff.cut.fctr:startprice.dcm2.is9,spdiff.cut.fctr:cellular.fctr,spdiff.cut.fctr:sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.685 0.439
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7639652 0.5824176 0.9455128 0.8969956
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9147287 0.8544868
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8260892 0.8955055 0.5516326
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7205116 0.4716981 0.9693252 0.8736399
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.9127907 0.8611111
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8248659 0.8923117 0.5764152
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0221758 0.06195974# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv &
(exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Low.cor.X",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Low.cor.X.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.chrs.pnct11.n.log,D.weight.post.stop.sum,D.chrs.pnct13.n.log,D.chrs.n.log,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,.rnorm,prdl.descr.my.fctr,startprice.dgt2.is9,storage.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,cellular.fctr,condition.fctr,startprice.dgt1.is9,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0196 on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 6000 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 60 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 3.215322612
## .rnorm
## -0.046351247
## D.chrs.pnct11.n.log
## -0.272464337
## D.wrds.stop.n.log
## 0.027654957
## cellular.fctr1
## 0.042291686
## cellular.fctrUnknown
## -0.998947833
## color.fctrGold
## 0.317017872
## color.fctrSpace Gray
## -0.030201797
## condition.fctrFor parts or not working
## -0.588273670
## condition.fctrSeller refurbished
## -0.941888375
## prdl.descr.my.fctriPad2#0
## 0.377830219
## prdl.descr.my.fctriPad3#0
## 0.527936498
## prdl.descr.my.fctriPad4#1
## -0.899757716
## prdl.descr.my.fctriPadAir#0
## 1.320844350
## prdl.descr.my.fctriPadAir#1
## 0.501430056
## prdl.descr.my.fctriPadAir2#0
## 0.951446978
## prdl.descr.my.fctriPadAir2#1
## 2.342202723
## prdl.descr.my.fctriPadmini#0
## -0.391033588
## prdl.descr.my.fctriPadmini#1
## -0.312171272
## prdl.descr.my.fctriPadmini2#0
## 0.201514907
## prdl.descr.my.fctriPadmini2#1
## -0.460956892
## prdl.descr.my.fctriPadmini3#0
## -0.434051519
## spdiff.cut.fctr(-100,-10]
## 0.029506443
## spdiff.cut.fctr(-10,-1]
## 0.659510870
## spdiff.cut.fctr(-1,0]
## 1.127846643
## spdiff.cut.fctr(0,1]
## 0.638721903
## spdiff.cut.fctr(1,10]
## 1.224403926
## spdiff.cut.fctr(10,100]
## 1.295044925
## spdiff.cut.fctr(100,1e+03]
## 1.977683204
## sprice.root2
## -0.200696465
## startprice.dgt1.is9
## -0.303653200
## startprice.dgt2.is9
## -0.003829025
## storage.fctr64
## -0.076658906
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 3.242322635
## .rnorm
## -0.052561111
## D.chrs.pnct11.n.log
## -0.295124125
## D.wrds.stop.n.log
## 0.042538305
## cellular.fctr1
## 0.056448019
## cellular.fctrUnknown
## -1.010073633
## color.fctrGold
## 0.364411477
## color.fctrSpace Gray
## -0.065286956
## color.fctrUnknown
## -0.008616585
## color.fctrWhite
## -0.012786588
## condition.fctrFor parts or not working
## -0.635437649
## condition.fctrSeller refurbished
## -0.984596052
## prdl.descr.my.fctriPad2#0
## 0.404139983
## prdl.descr.my.fctriPad3#0
## 0.572437277
## prdl.descr.my.fctriPad3#1
## 0.022093213
## prdl.descr.my.fctriPad4#1
## -0.897987323
## prdl.descr.my.fctriPadAir#0
## 1.414993617
## prdl.descr.my.fctriPadAir#1
## 0.548969093
## prdl.descr.my.fctriPadAir2#0
## 1.022164860
## prdl.descr.my.fctriPadAir2#1
## 2.466892229
## prdl.descr.my.fctriPadmini#0
## -0.388411895
## prdl.descr.my.fctriPadmini#1
## -0.326918055
## prdl.descr.my.fctriPadmini2#0
## 0.267499690
## prdl.descr.my.fctriPadmini2#1
## -0.479622460
## prdl.descr.my.fctriPadmini3#0
## -0.422123221
## spdiff.cut.fctr(-100,-10]
## 0.063423482
## spdiff.cut.fctr(-10,-1]
## 0.709414867
## spdiff.cut.fctr(-1,0]
## 1.207674835
## spdiff.cut.fctr(0,1]
## 0.712898396
## spdiff.cut.fctr(1,10]
## 1.293466166
## spdiff.cut.fctr(10,100]
## 1.350501544
## spdiff.cut.fctr(100,1e+03]
## 2.087862454
## sprice.root2
## -0.205560969
## startprice.dgt1.is9
## -0.308424429
## startprice.dgt2.is9
## -0.008430107
## storage.fctr64
## -0.097908186
## sold.fctr sold.fctr.predict.Low.cor.X.glmnet.N
## 1 N 57
## 2 Y 12
## sold.fctr.predict.Low.cor.X.glmnet.Y
## 1 34
## 2 300
## Prediction
## Reference N Y
## N 57 34
## Y 12 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.858561e-01 6.429644e-01 8.506945e-01 9.152098e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 6.037325e-09 1.959645e-03
## sold.fctr sold.fctr.predict.Low.cor.X.glmnet.N
## 1 N 41
## 2 Y 13
## sold.fctr.predict.Low.cor.X.glmnet.Y
## 1 65
## 2 313
## Prediction
## Reference N Y
## N 41 65
## Y 13 313
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.194444e-01 4.157303e-01 7.798653e-01 8.545804e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.670289e-04 7.713021e-09
## id
## 1 Low.cor.X.glmnet
## feats
## 1 spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.chrs.pnct11.n.log,D.weight.post.stop.sum,D.chrs.pnct13.n.log,D.chrs.n.log,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,.rnorm,prdl.descr.my.fctr,startprice.dgt2.is9,storage.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,cellular.fctr,condition.fctr,startprice.dgt1.is9,sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 10.304 0.462
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.793956 0.6263736 0.9615385 0.9310721
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9287926 0.8412075
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8506945 0.9152098 0.4975288
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6734576 0.3867925 0.9601227 0.8467126
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.8892045 0.8194444
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7798653 0.8545804 0.4157303
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01728829 0.04800516rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 10 fit.models 6 0 0 188.519 260.165 71.646
## 11 fit.models 6 1 1 260.166 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor="setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 270.399 NA NA#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glb_mdl_family_lst)) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = TRUE, label.minor = "setup")
indep_vars <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df, grepl(".glmnet", id, fixed=TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select most importance feature
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <- paste0(unlist(strsplit(topindep_var, ".fctr"))[1],
".fctr")
if (topindep_var %in% names(glb_interaction_only_feats_lst)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
importance_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df, importance > importance_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjust_interaction_feats(myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
indep_vars <- setdiff(indep_vars, topindep_var)
if (length(interact_vars) > 0) {
indep_vars <- setdiff(indep_vars, myextract_actual_feats(interact_vars))
indep_vars <- c(indep_vars,
paste(topindep_var, setdiff(interact_vars, topindep_var), sep = "*"))
} else indep_vars <- union(indep_vars, topindep_var)
}
}
if (is.null(indep_vars))
indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indep_vars))
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]])) {
if (!is.null(glb_mdl_family_lst[["Best.Interact"]]))
glb_mdl_family_lst[[mdl_id_pfx]] <- glb_mdl_family_lst[["Best.Interact"]]
}
}
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glb_mdl_family_lst[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars <- setdiff(indep_vars, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, tune.df = glb_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
#trainControl.allowParallel = FALSE,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 270.399 270.409
## 2 fit.models_1_RFE.X 2 0 setup 270.410 NA
## elapsed
## 1 0.01
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_RFE.X 2 0 setup 270.410 270.417
## 3 fit.models_1_RFE.X 2 1 glm 270.418 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "fitting model: RFE.X.glm"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr"
## + Fold1.Rep1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 204, 211, 246
## Warning: not plotting observations with leverage one:
## 204, 211, 246
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.48670 0.00001 0.04354 0.26921 2.92568
##
## Coefficients: (15 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 3.329e+02 1.480e+02 2.250
## .rnorm -3.159e-01 2.472e-01 -1.278
## D.chrs.n.log 1.781e+00 3.341e+00 0.533
## D.chrs.pnct11.n.log -1.995e+00 1.355e+00 -1.472
## D.chrs.pnct13.n.log 6.849e-01 9.465e-01 0.724
## D.ratio.weight.sum.wrds.n -6.737e-02 1.479e+00 -0.046
## D.ratio.wrds.stop.n.wrds.n -3.409e+01 1.255e+01 -2.716
## D.terms.post.stem.n.log 1.672e+01 1.900e+01 0.880
## D.terms.post.stop.n.log -2.473e+01 2.052e+01 -1.205
## D.weight.post.stem.sum 5.390e+01 2.599e+01 2.074
## D.weight.post.stop.sum -5.070e+01 2.543e+01 -1.994
## D.weight.sum NA NA NA
## D.weight.sum.stem.stop.Ratio -2.907e+02 1.450e+02 -2.004
## D.wrds.n.log -9.762e-01 5.645e+00 -0.173
## D.wrds.stop.n.log 5.252e+00 2.170e+00 2.420
## D.wrds.unq.n.log NA NA NA
## cellular.fctr1 9.736e-02 8.390e-01 0.116
## cellular.fctrUnknown -1.287e+00 1.360e+00 -0.947
## color.fctrGold 2.062e+00 1.545e+00 1.335
## `color.fctrSpace Gray` -1.191e+00 9.434e-01 -1.262
## color.fctrUnknown -1.465e-01 6.855e-01 -0.214
## color.fctrWhite -7.449e-01 6.962e-01 -1.070
## `condition.fctrFor parts or not working` -2.185e+00 8.245e-01 -2.650
## `condition.fctrManufacturer refurbished` 1.265e+00 2.445e+00 0.517
## condition.fctrNew 1.138e+00 9.867e-01 1.153
## `condition.fctrNew other (see details)` 1.203e+00 1.435e+00 0.839
## `condition.fctrSeller refurbished` -2.048e+00 1.227e+00 -1.668
## `prdl.descr.my.fctrUnknown#1` -3.407e+01 1.391e+01 -2.450
## `prdl.descr.my.fctriPad1#0` -3.533e-01 1.333e+00 -0.265
## `prdl.descr.my.fctriPad1#1` -3.328e+01 1.294e+01 -2.571
## `prdl.descr.my.fctriPad2#0` 1.525e+00 1.247e+00 1.223
## `prdl.descr.my.fctriPad2#1` -3.284e+01 1.278e+01 -2.569
## `prdl.descr.my.fctriPad3#0` 2.811e+00 1.226e+00 2.292
## `prdl.descr.my.fctriPad3#1` -3.219e+01 1.304e+01 -2.469
## `prdl.descr.my.fctriPad4#0` 2.030e+00 1.345e+00 1.510
## `prdl.descr.my.fctriPad4#1` -3.317e+01 1.310e+01 -2.531
## `prdl.descr.my.fctriPadAir#0` 5.284e+00 2.168e+00 2.437
## `prdl.descr.my.fctriPadAir#1` -3.019e+01 1.319e+01 -2.289
## `prdl.descr.my.fctriPadAir2#0` 3.396e+00 1.765e+00 1.924
## `prdl.descr.my.fctriPadAir2#1` -9.756e+00 2.258e+03 -0.004
## `prdl.descr.my.fctriPadmini#0` 5.619e-01 1.165e+00 0.483
## `prdl.descr.my.fctriPadmini#1` -3.418e+01 1.339e+01 -2.552
## `prdl.descr.my.fctriPadmini2#0` 3.289e+00 2.049e+00 1.605
## `prdl.descr.my.fctriPadmini2#1` -3.524e+01 2.660e+01 -1.325
## `prdl.descr.my.fctriPadmini3#0` 1.389e+00 1.739e+00 0.799
## `prdl.descr.my.fctriPadmini3#1` -4.902e+01 1.075e+04 -0.005
## `spdiff.cut.fctr(-100,-10]` 1.146e+00 7.502e-01 1.528
## `spdiff.cut.fctr(-10,-1]` 2.168e+00 1.063e+00 2.040
## `spdiff.cut.fctr(-1,0]` 1.990e+01 1.143e+03 0.017
## `spdiff.cut.fctr(0,1]` 1.997e+01 1.720e+03 0.012
## `spdiff.cut.fctr(1,10]` 3.872e+00 1.662e+00 2.329
## `spdiff.cut.fctr(10,100]` 3.016e+00 9.884e-01 3.051
## `spdiff.cut.fctr(100,1e+03]` 1.976e+01 4.808e+03 0.004
## sprice.d20nexp -4.479e+00 5.591e+00 -0.801
## sprice.log10 -4.091e-01 1.450e+00 -0.282
## sprice.root2 -4.576e-01 2.497e-01 -1.833
## startprice.dcm1.is9 -1.357e-01 1.084e+00 -0.125
## startprice.dcm2.is9 -3.037e-01 1.204e+00 -0.252
## startprice.dgt1.is9 2.476e-01 7.047e-01 0.351
## startprice.dgt2.is9 -6.476e-01 8.030e-01 -0.807
## storage.fctr16 -1.747e+00 1.438e+00 -1.215
## storage.fctr32 -9.666e-01 1.522e+00 -0.635
## storage.fctr64 -1.436e+00 1.320e+00 -1.088
## storage.fctrUnknown -6.032e-01 1.766e+00 -0.342
## `cellular.fctr0:carrier.fctrNone` NA NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA NA
## `cellular.fctr0:carrier.fctrOther` NA NA NA
## `cellular.fctr1:carrier.fctrOther` 1.271e+01 1.075e+04 0.001
## `cellular.fctrUnknown:carrier.fctrOther` NA NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA NA
## `cellular.fctr1:carrier.fctrSprint` 2.129e+01 1.075e+04 0.002
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` -1.316e+00 3.635e+00 -0.362
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA NA
## `cellular.fctr1:carrier.fctrUnknown` -1.996e-01 1.242e+00 -0.161
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA NA
## `cellular.fctr1:carrier.fctrVerizon` 1.690e-01 1.275e+00 0.133
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA NA
## Pr(>|z|)
## (Intercept) 0.02447 *
## .rnorm 0.20138
## D.chrs.n.log 0.59401
## D.chrs.pnct11.n.log 0.14092
## D.chrs.pnct13.n.log 0.46930
## D.ratio.weight.sum.wrds.n 0.96368
## D.ratio.wrds.stop.n.wrds.n 0.00660 **
## D.terms.post.stem.n.log 0.37892
## D.terms.post.stop.n.log 0.22820
## D.weight.post.stem.sum 0.03809 *
## D.weight.post.stop.sum 0.04614 *
## D.weight.sum NA
## D.weight.sum.stem.stop.Ratio 0.04507 *
## D.wrds.n.log 0.86272
## D.wrds.stop.n.log 0.01552 *
## D.wrds.unq.n.log NA
## cellular.fctr1 0.90761
## cellular.fctrUnknown 0.34372
## color.fctrGold 0.18199
## `color.fctrSpace Gray` 0.20692
## color.fctrUnknown 0.83080
## color.fctrWhite 0.28468
## `condition.fctrFor parts or not working` 0.00805 **
## `condition.fctrManufacturer refurbished` 0.60499
## condition.fctrNew 0.24874
## `condition.fctrNew other (see details)` 0.40169
## `condition.fctrSeller refurbished` 0.09527 .
## `prdl.descr.my.fctrUnknown#1` 0.01429 *
## `prdl.descr.my.fctriPad1#0` 0.79098
## `prdl.descr.my.fctriPad1#1` 0.01013 *
## `prdl.descr.my.fctriPad2#0` 0.22117
## `prdl.descr.my.fctriPad2#1` 0.01021 *
## `prdl.descr.my.fctriPad3#0` 0.02192 *
## `prdl.descr.my.fctriPad3#1` 0.01356 *
## `prdl.descr.my.fctriPad4#0` 0.13115
## `prdl.descr.my.fctriPad4#1` 0.01136 *
## `prdl.descr.my.fctriPadAir#0` 0.01482 *
## `prdl.descr.my.fctriPadAir#1` 0.02209 *
## `prdl.descr.my.fctriPadAir2#0` 0.05429 .
## `prdl.descr.my.fctriPadAir2#1` 0.99655
## `prdl.descr.my.fctriPadmini#0` 0.62943
## `prdl.descr.my.fctriPadmini#1` 0.01070 *
## `prdl.descr.my.fctriPadmini2#0` 0.10842
## `prdl.descr.my.fctriPadmini2#1` 0.18514
## `prdl.descr.my.fctriPadmini3#0` 0.42447
## `prdl.descr.my.fctriPadmini3#1` 0.99636
## `spdiff.cut.fctr(-100,-10]` 0.12656
## `spdiff.cut.fctr(-10,-1]` 0.04133 *
## `spdiff.cut.fctr(-1,0]` 0.98610
## `spdiff.cut.fctr(0,1]` 0.99074
## `spdiff.cut.fctr(1,10]` 0.01983 *
## `spdiff.cut.fctr(10,100]` 0.00228 **
## `spdiff.cut.fctr(100,1e+03]` 0.99672
## sprice.d20nexp 0.42305
## sprice.log10 0.77785
## sprice.root2 0.06685 .
## startprice.dcm1.is9 0.90035
## startprice.dcm2.is9 0.80088
## startprice.dgt1.is9 0.72530
## startprice.dgt2.is9 0.41993
## storage.fctr16 0.22444
## storage.fctr32 0.52531
## storage.fctr64 0.27647
## storage.fctrUnknown 0.73265
## `cellular.fctr0:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr0:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrOther` 0.99906
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr0:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrSprint` 0.99842
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.71725
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr0:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrUnknown` 0.87240
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr0:carrier.fctrVerizon` NA
## `cellular.fctr1:carrier.fctrVerizon` 0.89455
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 430.53 on 402 degrees of freedom
## Residual deviance: 173.03 on 336 degrees of freedom
## AIC: 307.03
##
## Number of Fisher Scoring iterations: 18## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.RFE.X.glm.N sold.fctr.predict.RFE.X.glm.Y
## 1 N 67 24
## 2 Y 13 299
## Prediction
## Reference N Y
## N 67 24
## Y 13 299
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.081886e-01 7.256637e-01 8.756722e-01 9.345315e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 1.273758e-12 1.001783e-01## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.RFE.X.glm.N sold.fctr.predict.RFE.X.glm.Y
## 1 N 42 64
## 2 Y 23 303
## Prediction
## Reference N Y
## N 42 64
## Y 23 303
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.986111e-01 3.745590e-01 7.576451e-01 8.354303e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 1.773132e-02 1.799242e-05
## id
## 1 RFE.X.glm
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.925 0.085
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8472985 0.7362637 0.9583333 0.9598126
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9417323 0.7824765
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8756722 0.9345315 0.3926215
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7009202 0.5 0.9018405 0.820639
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.8744589 0.7986111
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7576451 0.8354303 0.374559
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03034377 0.06479159
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_RFE.X 2 1 glm 270.418 275.956
## 4 fit.models_1_RFE.X 2 2 bayesglm 275.956 NA
## elapsed
## 3 5.538
## 4 NA
## [1] "fitting model: RFE.X.bayesglm"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: lme4
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.91168 0.02079 0.14844 0.45857 2.25811
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 7.980392 10.949649 0.729
## .rnorm -0.151986 0.185650 -0.819
## D.chrs.n.log -0.085731 0.503202 -0.170
## D.chrs.pnct11.n.log -0.821323 0.798743 -1.028
## D.chrs.pnct13.n.log 0.348423 0.684866 0.509
## D.ratio.weight.sum.wrds.n -0.779461 0.598413 -1.303
## D.ratio.wrds.stop.n.wrds.n -0.496968 2.272126 -0.219
## D.terms.post.stem.n.log -0.300653 0.916262 -0.328
## D.terms.post.stop.n.log -0.331565 0.919393 -0.361
## D.weight.post.stem.sum 0.195350 0.421623 0.463
## D.weight.post.stop.sum 0.164337 0.404557 0.406
## D.weight.sum 0.195350 0.421623 0.463
## D.weight.sum.stem.stop.Ratio -1.776213 10.633249 -0.167
## D.wrds.n.log -0.153561 0.833772 -0.184
## D.wrds.stop.n.log 0.411770 0.652214 0.631
## D.wrds.unq.n.log -0.300653 0.916262 -0.328
## cellular.fctr1 0.481550 1.352154 0.356
## cellular.fctrUnknown -0.765940 1.743735 -0.439
## color.fctrGold 0.801752 1.022867 0.784
## `color.fctrSpace Gray` -0.673752 0.688650 -0.978
## color.fctrUnknown -0.363019 0.500030 -0.726
## color.fctrWhite -0.535039 0.524313 -1.020
## `condition.fctrFor parts or not working` -1.496882 0.637753 -2.347
## `condition.fctrManufacturer refurbished` -0.038685 1.284320 -0.030
## condition.fctrNew 0.393059 0.716673 0.548
## `condition.fctrNew other (see details)` 0.284794 0.973469 0.293
## `condition.fctrSeller refurbished` -1.545403 0.858985 -1.799
## `prdl.descr.my.fctrUnknown#1` -0.252389 1.650987 -0.153
## `prdl.descr.my.fctriPad1#0` -0.582987 0.891632 -0.654
## `prdl.descr.my.fctriPad1#1` -0.549216 1.037849 -0.529
## `prdl.descr.my.fctriPad2#0` 0.639449 0.866930 0.738
## `prdl.descr.my.fctriPad2#1` -0.475259 0.961401 -0.494
## `prdl.descr.my.fctriPad3#0` 1.181540 0.780314 1.514
## `prdl.descr.my.fctriPad3#1` 0.150642 1.070836 0.141
## `prdl.descr.my.fctriPad4#0` 0.565830 0.804722 0.703
## `prdl.descr.my.fctriPad4#1` -0.763912 1.117147 -0.684
## `prdl.descr.my.fctriPadAir#0` 2.699698 1.217703 2.217
## `prdl.descr.my.fctriPadAir#1` 1.312077 1.116231 1.175
## `prdl.descr.my.fctriPadAir2#0` 2.110755 0.969033 2.178
## `prdl.descr.my.fctriPadAir2#1` 4.702400 2.027106 2.320
## `prdl.descr.my.fctriPadmini#0` -0.243129 0.722000 -0.337
## `prdl.descr.my.fctriPadmini#1` -0.682252 0.952269 -0.716
## `prdl.descr.my.fctriPadmini2#0` 0.933137 1.205687 0.774
## `prdl.descr.my.fctriPadmini2#1` -0.292406 2.045917 -0.143
## `prdl.descr.my.fctriPadmini3#0` -0.017739 1.106861 -0.016
## `prdl.descr.my.fctriPadmini3#1` -0.089483 2.318453 -0.039
## `spdiff.cut.fctr(-100,-10]` 0.545934 0.497342 1.098
## `spdiff.cut.fctr(-10,-1]` 1.285461 0.700286 1.836
## `spdiff.cut.fctr(-1,0]` 2.213534 1.591890 1.391
## `spdiff.cut.fctr(0,1]` 1.268980 1.660252 0.764
## `spdiff.cut.fctr(1,10]` 2.257910 1.016352 2.222
## `spdiff.cut.fctr(10,100]` 2.018824 0.659338 3.062
## `spdiff.cut.fctr(100,1e+03]` 2.407108 1.662689 1.448
## sprice.d20nexp 0.176562 1.920106 0.092
## sprice.log10 -0.226900 0.421825 -0.538
## sprice.root2 -0.305067 0.100222 -3.044
## startprice.dcm1.is9 -0.106882 0.749563 -0.143
## startprice.dcm2.is9 0.091468 0.801627 0.114
## startprice.dgt1.is9 -0.271192 0.534929 -0.507
## startprice.dgt2.is9 -0.065056 0.602180 -0.108
## storage.fctr16 -0.712579 0.765319 -0.931
## storage.fctr32 -0.322592 0.824515 -0.391
## storage.fctr64 -0.715910 0.751522 -0.953
## storage.fctrUnknown 0.195443 1.013592 0.193
## `cellular.fctr0:carrier.fctrNone` 0.157936 1.335470 0.118
## `cellular.fctr1:carrier.fctrNone` 0.000000 2.500000 0.000
## `cellular.fctrUnknown:carrier.fctrNone` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrOther` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrOther` 0.205612 2.173332 0.095
## `cellular.fctrUnknown:carrier.fctrOther` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrSprint` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrSprint` 1.309206 1.749905 0.748
## `cellular.fctrUnknown:carrier.fctrSprint` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrT-Mobile` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrT-Mobile` -0.753542 1.478753 -0.510
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrUnknown` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrUnknown` -0.363013 0.816388 -0.445
## `cellular.fctrUnknown:carrier.fctrUnknown` -0.765940 1.743735 -0.439
## `cellular.fctr0:carrier.fctrVerizon` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrVerizon` 0.009391 0.887813 0.011
## `cellular.fctrUnknown:carrier.fctrVerizon` 0.000000 2.500000 0.000
## Pr(>|z|)
## (Intercept) 0.46611
## .rnorm 0.41298
## D.chrs.n.log 0.86472
## D.chrs.pnct11.n.log 0.30382
## D.chrs.pnct13.n.log 0.61093
## D.ratio.weight.sum.wrds.n 0.19273
## D.ratio.wrds.stop.n.wrds.n 0.82687
## D.terms.post.stem.n.log 0.74281
## D.terms.post.stop.n.log 0.71837
## D.weight.post.stem.sum 0.64313
## D.weight.post.stop.sum 0.68458
## D.weight.sum 0.64313
## D.weight.sum.stem.stop.Ratio 0.86734
## D.wrds.n.log 0.85387
## D.wrds.stop.n.log 0.52782
## D.wrds.unq.n.log 0.74281
## cellular.fctr1 0.72174
## cellular.fctrUnknown 0.66048
## color.fctrGold 0.43314
## `color.fctrSpace Gray` 0.32789
## color.fctrUnknown 0.46784
## color.fctrWhite 0.30751
## `condition.fctrFor parts or not working` 0.01892 *
## `condition.fctrManufacturer refurbished` 0.97597
## condition.fctrNew 0.58338
## `condition.fctrNew other (see details)` 0.76986
## `condition.fctrSeller refurbished` 0.07200 .
## `prdl.descr.my.fctrUnknown#1` 0.87850
## `prdl.descr.my.fctriPad1#0` 0.51321
## `prdl.descr.my.fctriPad1#1` 0.59668
## `prdl.descr.my.fctriPad2#0` 0.46076
## `prdl.descr.my.fctriPad2#1` 0.62107
## `prdl.descr.my.fctriPad3#0` 0.12998
## `prdl.descr.my.fctriPad3#1` 0.88813
## `prdl.descr.my.fctriPad4#0` 0.48197
## `prdl.descr.my.fctriPad4#1` 0.49410
## `prdl.descr.my.fctriPadAir#0` 0.02662 *
## `prdl.descr.my.fctriPadAir#1` 0.23981
## `prdl.descr.my.fctriPadAir2#0` 0.02939 *
## `prdl.descr.my.fctriPadAir2#1` 0.02035 *
## `prdl.descr.my.fctriPadmini#0` 0.73631
## `prdl.descr.my.fctriPadmini#1` 0.47371
## `prdl.descr.my.fctriPadmini2#0` 0.43896
## `prdl.descr.my.fctriPadmini2#1` 0.88635
## `prdl.descr.my.fctriPadmini3#0` 0.98721
## `prdl.descr.my.fctriPadmini3#1` 0.96921
## `spdiff.cut.fctr(-100,-10]` 0.27233
## `spdiff.cut.fctr(-10,-1]` 0.06641 .
## `spdiff.cut.fctr(-1,0]` 0.16437
## `spdiff.cut.fctr(0,1]` 0.44467
## `spdiff.cut.fctr(1,10]` 0.02631 *
## `spdiff.cut.fctr(10,100]` 0.00220 **
## `spdiff.cut.fctr(100,1e+03]` 0.14770
## sprice.d20nexp 0.92673
## sprice.log10 0.59065
## sprice.root2 0.00234 **
## startprice.dcm1.is9 0.88661
## startprice.dcm2.is9 0.90916
## startprice.dgt1.is9 0.61218
## startprice.dgt2.is9 0.91397
## storage.fctr16 0.35181
## storage.fctr32 0.69561
## storage.fctr64 0.34079
## storage.fctrUnknown 0.84710
## `cellular.fctr0:carrier.fctrNone` 0.90586
## `cellular.fctr1:carrier.fctrNone` 1.00000
## `cellular.fctrUnknown:carrier.fctrNone` 1.00000
## `cellular.fctr0:carrier.fctrOther` 1.00000
## `cellular.fctr1:carrier.fctrOther` 0.92463
## `cellular.fctrUnknown:carrier.fctrOther` 1.00000
## `cellular.fctr0:carrier.fctrSprint` 1.00000
## `cellular.fctr1:carrier.fctrSprint` 0.45436
## `cellular.fctrUnknown:carrier.fctrSprint` 1.00000
## `cellular.fctr0:carrier.fctrT-Mobile` 1.00000
## `cellular.fctr1:carrier.fctrT-Mobile` 0.61035
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 1.00000
## `cellular.fctr0:carrier.fctrUnknown` 1.00000
## `cellular.fctr1:carrier.fctrUnknown` 0.65657
## `cellular.fctrUnknown:carrier.fctrUnknown` 0.66048
## `cellular.fctr0:carrier.fctrVerizon` 1.00000
## `cellular.fctr1:carrier.fctrVerizon` 0.99156
## `cellular.fctrUnknown:carrier.fctrVerizon` 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 430.53 on 402 degrees of freedom
## Residual deviance: 196.33 on 321 degrees of freedom
## AIC: 360.33
##
## Number of Fisher Scoring iterations: 22
## sold.fctr sold.fctr.predict.RFE.X.bayesglm.N
## 1 N 63
## 2 Y 12
## sold.fctr.predict.RFE.X.bayesglm.Y
## 1 28
## 2 300
## Prediction
## Reference N Y
## N 63 28
## Y 12 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.007444e-01 6.972656e-01 8.672986e-01 9.281401e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.717294e-11 1.770607e-02
## sold.fctr sold.fctr.predict.RFE.X.bayesglm.N
## 1 N 56
## 2 Y 22
## sold.fctr.predict.RFE.X.bayesglm.Y
## 1 50
## 2 304
## Prediction
## Reference N Y
## N 56 50
## Y 22 304
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.333333e-01 5.059093e-01 7.947758e-01 8.672487e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 4.917603e-05 1.462717e-03
## id
## 1 RFE.X.bayesglm
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 3.193 0.169
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8269231 0.6923077 0.9615385 0.9455128
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9375 0.8337694
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8672986 0.9281401 0.5153729
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7304086 0.5283019 0.9325153 0.8568411
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.8941176 0.8333333
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7947758 0.8672487 0.5059093
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02818922 0.05806897
## label step_major step_minor label_minor bgn end
## 4 fit.models_1_RFE.X 2 2 bayesglm 275.956 282.164
## 5 fit.models_1_RFE.X 2 3 glmnet 282.165 NA
## elapsed
## 4 6.208
## 5 NA
## [1] "fitting model: RFE.X.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0196 on full training set
## Length Class Mode
## a0 99 -none- numeric
## beta 8019 dgCMatrix S4
## df 99 -none- numeric
## dim 2 -none- numeric
## lambda 99 -none- numeric
## dev.ratio 99 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 81 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 3.64476130
## .rnorm
## -0.04715339
## D.chrs.pnct11.n.log
## -0.27178606
## D.wrds.stop.n.log
## 0.01060042
## cellular.fctrUnknown
## -0.52953985
## color.fctrGold
## 0.33200372
## color.fctrSpace Gray
## -0.09603546
## condition.fctrFor parts or not working
## -0.63433303
## condition.fctrSeller refurbished
## -0.98900984
## prdl.descr.my.fctriPad2#0
## 0.37476078
## prdl.descr.my.fctriPad3#0
## 0.52965983
## prdl.descr.my.fctriPad4#1
## -0.87493058
## prdl.descr.my.fctriPadAir#0
## 1.35391787
## prdl.descr.my.fctriPadAir#1
## 0.56987151
## prdl.descr.my.fctriPadAir2#0
## 0.81133923
## prdl.descr.my.fctriPadAir2#1
## 2.26536539
## prdl.descr.my.fctriPadmini#0
## -0.40026955
## prdl.descr.my.fctriPadmini#1
## -0.30746965
## prdl.descr.my.fctriPadmini2#0
## 0.24694801
## prdl.descr.my.fctriPadmini2#1
## -0.40192512
## prdl.descr.my.fctriPadmini3#0
## -0.48749579
## spdiff.cut.fctr(-100,-10]
## 0.09123465
## spdiff.cut.fctr(-10,-1]
## 0.69202823
## spdiff.cut.fctr(-1,0]
## 0.76441707
## spdiff.cut.fctr(0,1]
## 0.23611798
## spdiff.cut.fctr(1,10]
## 1.20160963
## spdiff.cut.fctr(10,100]
## 1.33218141
## spdiff.cut.fctr(100,1e+03]
## 2.03171518
## sprice.d20nexp
## 0.44484189
## sprice.log10
## -0.18966684
## sprice.root2
## -0.16335923
## startprice.dgt1.is9
## -0.29135197
## storage.fctr64
## -0.05396287
## cellular.fctr1:carrier.fctrSprint
## 1.70187130
## cellular.fctr1:carrier.fctrT-Mobile
## -0.37049649
## cellular.fctr1:carrier.fctrUnknown
## -0.03820746
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.52707167
## cellular.fctr1:carrier.fctrVerizon
## 0.15853089
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 3.677862404
## .rnorm
## -0.053647319
## D.chrs.pnct11.n.log
## -0.292500157
## D.wrds.stop.n.log
## 0.026964620
## cellular.fctr1
## 0.014741599
## cellular.fctrUnknown
## -0.534274721
## color.fctrGold
## 0.388246919
## color.fctrSpace Gray
## -0.136073978
## color.fctrUnknown
## -0.001838048
## condition.fctrFor parts or not working
## -0.682089508
## condition.fctrSeller refurbished
## -1.030680573
## prdl.descr.my.fctriPad2#0
## 0.400153344
## prdl.descr.my.fctriPad3#0
## 0.573981439
## prdl.descr.my.fctriPad4#1
## -0.878771328
## prdl.descr.my.fctriPadAir#0
## 1.461628835
## prdl.descr.my.fctriPadAir#1
## 0.632815937
## prdl.descr.my.fctriPadAir2#0
## 0.870119341
## prdl.descr.my.fctriPadAir2#1
## 2.379282285
## prdl.descr.my.fctriPadmini#0
## -0.399160497
## prdl.descr.my.fctriPadmini#1
## -0.326318251
## prdl.descr.my.fctriPadmini2#0
## 0.318996175
## prdl.descr.my.fctriPadmini2#1
## -0.411610833
## prdl.descr.my.fctriPadmini3#0
## -0.478878793
## spdiff.cut.fctr(-100,-10]
## 0.124043154
## spdiff.cut.fctr(-10,-1]
## 0.740186901
## spdiff.cut.fctr(-1,0]
## 0.839914557
## spdiff.cut.fctr(0,1]
## 0.308327034
## spdiff.cut.fctr(1,10]
## 1.269856795
## spdiff.cut.fctr(10,100]
## 1.388885563
## spdiff.cut.fctr(100,1e+03]
## 2.147602530
## sprice.d20nexp
## 0.445759244
## sprice.log10
## -0.193783186
## sprice.root2
## -0.167302133
## startprice.dgt1.is9
## -0.300020502
## storage.fctr64
## -0.072181044
## cellular.fctr1:carrier.fctrSprint
## 1.834493622
## cellular.fctr1:carrier.fctrT-Mobile
## -0.479004489
## cellular.fctr1:carrier.fctrUnknown
## -0.047367063
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.531445887
## cellular.fctr1:carrier.fctrVerizon
## 0.161705067
## sold.fctr sold.fctr.predict.RFE.X.glmnet.N
## 1 N 60
## 2 Y 12
## sold.fctr.predict.RFE.X.glmnet.Y
## 1 31
## 2 300
## Prediction
## Reference N Y
## N 60 31
## Y 12 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.933002e-01 6.704574e-01 8.589742e-01 9.216980e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 4.540937e-10 6.051564e-03
## sold.fctr sold.fctr.predict.RFE.X.glmnet.N
## 1 N 43
## 2 Y 14
## sold.fctr.predict.RFE.X.glmnet.Y
## 1 63
## 2 312
## Prediction
## Reference N Y
## N 43 63
## Y 14 312
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.217593e-01 4.297470e-01 7.823447e-01 8.566975e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 5.040175e-04 4.497623e-08
## id
## 1 RFE.X.glmnet
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 11.142 0.713
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8104396 0.6593407 0.9615385 0.9328684
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.933126 0.8445059
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8589742 0.921698 0.5106639
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6813578 0.4056604 0.9570552 0.8419956
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.8901569 0.8217593
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7823447 0.8566975 0.429747
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0179705 0.05239318
## label step_major step_minor label_minor bgn end
## 5 fit.models_1_RFE.X 2 3 glmnet 282.165 297.179
## 6 fit.models_1_RFE.X 2 4 rpart 297.179 NA
## elapsed
## 5 15.014
## 6 NA
## [1] "fitting model: RFE.X.rpart"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr"
## + Fold1.Rep1: cp=0
## - Fold1.Rep1: cp=0
## + Fold2.Rep1: cp=0
## - Fold2.Rep1: cp=0
## + Fold3.Rep1: cp=0
## - Fold3.Rep1: cp=0
## + Fold1.Rep2: cp=0
## - Fold1.Rep2: cp=0
## + Fold2.Rep2: cp=0
## - Fold2.Rep2: cp=0
## + Fold3.Rep2: cp=0
## - Fold3.Rep2: cp=0
## + Fold1.Rep3: cp=0
## - Fold1.Rep3: cp=0
## + Fold2.Rep3: cp=0
## - Fold2.Rep3: cp=0
## + Fold3.Rep3: cp=0
## - Fold3.Rep3: cp=0
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.033 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 403
##
## CP nsplit rel error
## 1 0.13186813 0 1.0000000
## 2 0.03296703 2 0.7362637
##
## Variable importance
## sprice.d20nexp
## 28
## sprice.log10
## 28
## sprice.root2
## 28
## spdiff.cut.fctr(10,100]
## 9
## condition.fctrNew
## 5
## prdl.descr.my.fctriPadAir2#0
## 3
## condition.fctrNew other (see details)
## 1
##
## Node number 1: 403 observations, complexity param=0.1318681
## predicted class=Y expected loss=0.2258065 P(node) =1
## class counts: 91 312
## probabilities: 0.226 0.774
## left son=2 (136 obs) right son=3 (267 obs)
## Primary splits:
## sprice.log10 < 4.959152 to the right, improve=34.265420, (0 missing)
## sprice.root2 < 11.93667 to the right, improve=34.265420, (0 missing)
## sprice.d20nexp < 0.0008112562 to the left, improve=34.265420, (0 missing)
## spdiff.cut.fctr(-1,0] < 0.5 to the left, improve= 6.632332, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 5.153866, (0 missing)
## Surrogate splits:
## sprice.d20nexp < 0.0008112562 to the left, agree=1.000, adj=1.000, (0 split)
## sprice.root2 < 11.93667 to the right, agree=1.000, adj=1.000, (0 split)
## condition.fctrNew < 0.5 to the right, agree=0.720, adj=0.169, (0 split)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the right, agree=0.700, adj=0.110, (0 split)
## condition.fctrNew other (see details) < 0.5 to the right, agree=0.672, adj=0.029, (0 split)
##
## Node number 2: 136 observations, complexity param=0.1318681
## predicted class=N expected loss=0.4852941 P(node) =0.337469
## class counts: 70 66
## probabilities: 0.515 0.485
## left son=4 (112 obs) right son=5 (24 obs)
## Primary splits:
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve=10.845940, (0 missing)
## cellular.fctrUnknown < 0.5 to the right, improve= 4.176989, (0 missing)
## cellular.fctrUnknown:carrier.fctrUnknown < 0.5 to the right, improve= 4.176989, (0 missing)
## sprice.d20nexp < 1.139199e-09 to the left, improve= 3.001316, (0 missing)
## sprice.log10 < 6.044769 to the right, improve= 3.001316, (0 missing)
##
## Node number 3: 267 observations
## predicted class=Y expected loss=0.07865169 P(node) =0.662531
## class counts: 21 246
## probabilities: 0.079 0.921
##
## Node number 4: 112 observations
## predicted class=N expected loss=0.3928571 P(node) =0.2779156
## class counts: 68 44
## probabilities: 0.607 0.393
##
## Node number 5: 24 observations
## predicted class=Y expected loss=0.08333333 P(node) =0.05955335
## class counts: 2 22
## probabilities: 0.083 0.917
##
## n= 403
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 403 91 Y (0.22580645 0.77419355)
## 2) sprice.log10>=4.959152 136 66 N (0.51470588 0.48529412)
## 4) spdiff.cut.fctr(10,100]< 0.5 112 44 N (0.60714286 0.39285714) *
## 5) spdiff.cut.fctr(10,100]>=0.5 24 2 Y (0.08333333 0.91666667) *
## 3) sprice.log10< 4.959152 267 21 Y (0.07865169 0.92134831) *
## sold.fctr sold.fctr.predict.RFE.X.rpart.N
## 1 N 68
## 2 Y 44
## sold.fctr.predict.RFE.X.rpart.Y
## 1 23
## 2 268
## Prediction
## Reference N Y
## N 68 23
## Y 44 268
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.833746898 0.560423280 0.793733671 0.868771672 0.774193548
## AccuracyPValue McnemarPValue
## 0.001950422 0.014550049
## sold.fctr sold.fctr.predict.RFE.X.rpart.Y
## 1 N 106
## 2 Y 326
## Prediction
## Reference N Y
## N 0 106
## Y 0 326
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.546296e-01 0.000000e+00 7.112313e-01 7.945009e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 5.260575e-01 2.013550e-24
## id
## 1 RFE.X.rpart
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 2.462 0.039
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8031136 0.7472527 0.8589744 0.8036419
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9 0.8888889 0.8147595
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7937337 0.8687717 0.443185
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7600995 0.6981132 0.8220859 0.754254
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.8601583 0.7546296
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7112313 0.7945009 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02210169 0.08736425
## label step_major step_minor label_minor bgn end
## 6 fit.models_1_RFE.X 2 4 rpart 297.179 303.641
## 7 fit.models_1_RFE.X 2 5 gbm 303.642 NA
## elapsed
## 6 6.462
## 7 NA
## [1] "fitting model: RFE.X.gbm"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr"## Loading required package: gbm
## Loading required package: splines
## Loaded gbm 2.1.1## Aggregating results
## Selecting tuning parameters
## Fitting n.trees = 50, interaction.depth = 4, shrinkage = 0.1, n.minobsinnode = 10 on full training set## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 64: cellular.fctr1:carrier.fctrNone has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 65: cellular.fctrUnknown:carrier.fctrNone has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 66: cellular.fctr0:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 68: cellular.fctrUnknown:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 69: cellular.fctr0:carrier.fctrSprint has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 71: cellular.fctrUnknown:carrier.fctrSprint has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 72: cellular.fctr0:carrier.fctrT-Mobile has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 74: cellular.fctrUnknown:carrier.fctrT-Mobile has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 75: cellular.fctr0:carrier.fctrUnknown has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 78: cellular.fctr0:carrier.fctrVerizon has no variation.## Warning in gbm.fit(x = structure(c(4.61512051684126, 0, 4.58496747867057, :
## variable 80: cellular.fctrUnknown:carrier.fctrVerizon has no variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0180 nan 0.1000 0.0195
## 2 0.9675 nan 0.1000 0.0200
## 3 0.9264 nan 0.1000 0.0179
## 4 0.8801 nan 0.1000 0.0164
## 5 0.8512 nan 0.1000 0.0149
## 6 0.8266 nan 0.1000 0.0100
## 7 0.8055 nan 0.1000 0.0066
## 8 0.7839 nan 0.1000 0.0043
## 9 0.7640 nan 0.1000 0.0061
## 10 0.7500 nan 0.1000 0.0050
## 20 0.6399 nan 0.1000 0.0013
## 40 0.5273 nan 0.1000 -0.0039
## 50 0.4969 nan 0.1000 -0.0003## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: n.trees## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: shrinkage## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: n.minobsinnode
## var
## sprice.log10 sprice.log10
## sprice.d20nexp sprice.d20nexp
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(10,100]
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio
## spdiff.cut.fctr(-100,-10] spdiff.cut.fctr(-100,-10]
## cellular.fctrUnknown cellular.fctrUnknown
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#0
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n
## condition.fctrFor parts or not working condition.fctrFor parts or not working
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n
## startprice.dgt1.is9 startprice.dgt1.is9
## startprice.dcm1.is9 startprice.dcm1.is9
## D.weight.post.stem.sum D.weight.post.stem.sum
## startprice.dcm2.is9 startprice.dcm2.is9
## D.chrs.n.log D.chrs.n.log
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#0
## D.weight.post.stop.sum D.weight.post.stop.sum
## cellular.fctr1:carrier.fctrUnknown cellular.fctr1:carrier.fctrUnknown
## condition.fctrSeller refurbished condition.fctrSeller refurbished
## storage.fctrUnknown storage.fctrUnknown
## cellular.fctr0:carrier.fctrNone cellular.fctr0:carrier.fctrNone
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log
## cellular.fctr1 cellular.fctr1
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log
## color.fctrWhite color.fctrWhite
## prdl.descr.my.fctriPad3#0 prdl.descr.my.fctriPad3#0
## D.terms.post.stop.n.log D.terms.post.stop.n.log
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-10,-1]
## storage.fctr64 storage.fctr64
## startprice.dgt2.is9 startprice.dgt2.is9
## D.terms.post.stem.n.log D.terms.post.stem.n.log
## color.fctrSpace Gray color.fctrSpace Gray
## condition.fctrNew condition.fctrNew
## color.fctrUnknown color.fctrUnknown
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#0
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad1#1
## prdl.descr.my.fctriPadmini#1 prdl.descr.my.fctriPadmini#1
## D.weight.sum D.weight.sum
## D.wrds.n.log D.wrds.n.log
## D.wrds.stop.n.log D.wrds.stop.n.log
## D.wrds.unq.n.log D.wrds.unq.n.log
## color.fctrGold color.fctrGold
## condition.fctrManufacturer refurbished condition.fctrManufacturer refurbished
## condition.fctrNew other (see details) condition.fctrNew other (see details)
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctrUnknown#1
## prdl.descr.my.fctriPad1#0 prdl.descr.my.fctriPad1#0
## prdl.descr.my.fctriPad2#0 prdl.descr.my.fctriPad2#0
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad2#1
## prdl.descr.my.fctriPad3#1 prdl.descr.my.fctriPad3#1
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#0
## prdl.descr.my.fctriPad4#1 prdl.descr.my.fctriPad4#1
## prdl.descr.my.fctriPadAir#1 prdl.descr.my.fctriPadAir#1
## prdl.descr.my.fctriPadAir2#1 prdl.descr.my.fctriPadAir2#1
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#0
## prdl.descr.my.fctriPadmini2#1 prdl.descr.my.fctriPadmini2#1
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#0
## prdl.descr.my.fctriPadmini3#1 prdl.descr.my.fctriPadmini3#1
## spdiff.cut.fctr(-1,0] spdiff.cut.fctr(-1,0]
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(0,1]
## spdiff.cut.fctr(1,10] spdiff.cut.fctr(1,10]
## spdiff.cut.fctr(100,1e+03] spdiff.cut.fctr(100,1e+03]
## sprice.root2 sprice.root2
## storage.fctr16 storage.fctr16
## storage.fctr32 storage.fctr32
## cellular.fctr1:carrier.fctrNone cellular.fctr1:carrier.fctrNone
## cellular.fctrUnknown:carrier.fctrNone cellular.fctrUnknown:carrier.fctrNone
## cellular.fctr0:carrier.fctrOther cellular.fctr0:carrier.fctrOther
## cellular.fctr1:carrier.fctrOther cellular.fctr1:carrier.fctrOther
## cellular.fctrUnknown:carrier.fctrOther cellular.fctrUnknown:carrier.fctrOther
## cellular.fctr0:carrier.fctrSprint cellular.fctr0:carrier.fctrSprint
## cellular.fctr1:carrier.fctrSprint cellular.fctr1:carrier.fctrSprint
## cellular.fctrUnknown:carrier.fctrSprint cellular.fctrUnknown:carrier.fctrSprint
## cellular.fctr0:carrier.fctrT-Mobile cellular.fctr0:carrier.fctrT-Mobile
## cellular.fctr1:carrier.fctrT-Mobile cellular.fctr1:carrier.fctrT-Mobile
## cellular.fctrUnknown:carrier.fctrT-Mobile cellular.fctrUnknown:carrier.fctrT-Mobile
## cellular.fctr0:carrier.fctrUnknown cellular.fctr0:carrier.fctrUnknown
## cellular.fctrUnknown:carrier.fctrUnknown cellular.fctrUnknown:carrier.fctrUnknown
## cellular.fctr0:carrier.fctrVerizon cellular.fctr0:carrier.fctrVerizon
## cellular.fctr1:carrier.fctrVerizon cellular.fctr1:carrier.fctrVerizon
## cellular.fctrUnknown:carrier.fctrVerizon cellular.fctrUnknown:carrier.fctrVerizon
## rel.inf
## sprice.log10 26.8385217
## sprice.d20nexp 22.8060192
## spdiff.cut.fctr(10,100] 7.1545962
## D.weight.sum.stem.stop.Ratio 5.4971295
## spdiff.cut.fctr(-100,-10] 3.9226068
## cellular.fctrUnknown 3.2269440
## prdl.descr.my.fctriPadAir2#0 2.7632749
## D.ratio.weight.sum.wrds.n 2.7625871
## condition.fctrFor parts or not working 2.4756002
## D.ratio.wrds.stop.n.wrds.n 2.3439993
## startprice.dgt1.is9 1.8953703
## startprice.dcm1.is9 1.8840046
## D.weight.post.stem.sum 1.6780925
## startprice.dcm2.is9 1.5152768
## D.chrs.n.log 1.3355341
## prdl.descr.my.fctriPadmini#0 1.2124640
## D.weight.post.stop.sum 1.2006663
## cellular.fctr1:carrier.fctrUnknown 0.9852238
## condition.fctrSeller refurbished 0.8822527
## storage.fctrUnknown 0.8797735
## cellular.fctr0:carrier.fctrNone 0.8138848
## D.chrs.pnct11.n.log 0.7542887
## cellular.fctr1 0.7122023
## D.chrs.pnct13.n.log 0.5741178
## color.fctrWhite 0.4632547
## prdl.descr.my.fctriPad3#0 0.4536772
## D.terms.post.stop.n.log 0.3935627
## spdiff.cut.fctr(-10,-1] 0.3482118
## storage.fctr64 0.3465695
## startprice.dgt2.is9 0.3280131
## D.terms.post.stem.n.log 0.2849832
## color.fctrSpace Gray 0.2669205
## condition.fctrNew 0.2327218
## color.fctrUnknown 0.2217392
## prdl.descr.my.fctriPadAir#0 0.2070151
## prdl.descr.my.fctriPad1#1 0.2057275
## prdl.descr.my.fctriPadmini#1 0.1331726
## D.weight.sum 0.0000000
## D.wrds.n.log 0.0000000
## D.wrds.stop.n.log 0.0000000
## D.wrds.unq.n.log 0.0000000
## color.fctrGold 0.0000000
## condition.fctrManufacturer refurbished 0.0000000
## condition.fctrNew other (see details) 0.0000000
## prdl.descr.my.fctrUnknown#1 0.0000000
## prdl.descr.my.fctriPad1#0 0.0000000
## prdl.descr.my.fctriPad2#0 0.0000000
## prdl.descr.my.fctriPad2#1 0.0000000
## prdl.descr.my.fctriPad3#1 0.0000000
## prdl.descr.my.fctriPad4#0 0.0000000
## prdl.descr.my.fctriPad4#1 0.0000000
## prdl.descr.my.fctriPadAir#1 0.0000000
## prdl.descr.my.fctriPadAir2#1 0.0000000
## prdl.descr.my.fctriPadmini2#0 0.0000000
## prdl.descr.my.fctriPadmini2#1 0.0000000
## prdl.descr.my.fctriPadmini3#0 0.0000000
## prdl.descr.my.fctriPadmini3#1 0.0000000
## spdiff.cut.fctr(-1,0] 0.0000000
## spdiff.cut.fctr(0,1] 0.0000000
## spdiff.cut.fctr(1,10] 0.0000000
## spdiff.cut.fctr(100,1e+03] 0.0000000
## sprice.root2 0.0000000
## storage.fctr16 0.0000000
## storage.fctr32 0.0000000
## cellular.fctr1:carrier.fctrNone 0.0000000
## cellular.fctrUnknown:carrier.fctrNone 0.0000000
## cellular.fctr0:carrier.fctrOther 0.0000000
## cellular.fctr1:carrier.fctrOther 0.0000000
## cellular.fctrUnknown:carrier.fctrOther 0.0000000
## cellular.fctr0:carrier.fctrSprint 0.0000000
## cellular.fctr1:carrier.fctrSprint 0.0000000
## cellular.fctrUnknown:carrier.fctrSprint 0.0000000
## cellular.fctr0:carrier.fctrT-Mobile 0.0000000
## cellular.fctr1:carrier.fctrT-Mobile 0.0000000
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.0000000
## cellular.fctr0:carrier.fctrUnknown 0.0000000
## cellular.fctrUnknown:carrier.fctrUnknown 0.0000000
## cellular.fctr0:carrier.fctrVerizon 0.0000000
## cellular.fctr1:carrier.fctrVerizon 0.0000000
## cellular.fctrUnknown:carrier.fctrVerizon 0.0000000
## sold.fctr sold.fctr.predict.RFE.X.gbm.N sold.fctr.predict.RFE.X.gbm.Y
## 1 N 61 30
## 2 Y 10 302
## Prediction
## Reference N Y
## N 61 30
## Y 10 302
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.007444e-01 6.921549e-01 8.672986e-01 9.281401e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.717294e-11 2.663119e-03
## sold.fctr sold.fctr.predict.RFE.X.gbm.N sold.fctr.predict.RFE.X.gbm.Y
## 1 N 45 61
## 2 Y 22 304
## Prediction
## Reference N Y
## N 45 61
## Y 22 304
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.078704e-01 4.076522e-01 7.675003e-01 8.439622e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 5.040800e-03 3.032101e-05
## id
## 1 RFE.X.gbm
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 5.096 0.206
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8191392 0.6703297 0.9679487 0.9553571
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9378882 0.8221792
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8672986 0.9281401 0.4498412
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6785218 0.4245283 0.9325153 0.8382625
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.8798842 0.8078704
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7675003 0.8439622 0.4076522
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01749072 0.05864702
## label step_major step_minor label_minor bgn end
## 7 fit.models_1_RFE.X 2 5 gbm 303.642 312.614
## 8 fit.models_1_RFE.X 2 6 rf 312.615 NA
## elapsed
## 7 8.972
## 8 NA
## [1] "fitting model: RFE.X.rf"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr"## Loading required package: randomForest
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
##
## The following object is masked from 'package:ggplot2':
##
## margin
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 60 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 403 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 806 matrix numeric
## oob.times 403 -none- numeric
## classes 2 -none- character
## importance 80 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 403 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 80 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## sold.fctr sold.fctr.predict.RFE.X.rf.N sold.fctr.predict.RFE.X.rf.Y
## 1 N 91 NA
## 2 Y 1 311
## Prediction
## Reference N Y
## N 91 0
## Y 1 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.975186e-01 9.929304e-01 9.862527e-01 9.999372e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 1.906509e-43 1.000000e+00
## sold.fctr sold.fctr.predict.RFE.X.rf.N sold.fctr.predict.RFE.X.rf.Y
## 1 N 33 73
## 2 Y 6 320
## Prediction
## Reference N Y
## N 33 73
## Y 6 320
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.171296e-01 3.723240e-01 7.773880e-01 8.524611e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 1.150241e-03 1.122867e-13
## id
## 1 RFE.X.rf
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 3.89 1.417
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9983974 1 0.9967949 0.9999824
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.9983949 0.8188586
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9862527 0.9999372 0.4631079
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7465563 0.5943396 0.898773 0.8557414
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.8901252 0.8171296
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.777388 0.8524611 0.372324
## label step_major step_minor label_minor bgn end
## 8 fit.models_1_RFE.X 2 6 rf 312.615 319.973
## 9 fit.models_1_RFE.X 2 7 nnet 319.974 NA
## elapsed
## 8 7.359
## 9 NA
## [1] "fitting model: RFE.X.nnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr"## Loading required package: nnet
## Aggregating results
## Selecting tuning parameters
## Fitting size = 1, decay = 0.1 on full training set
## # weights: 83
## initial value 245.862572
## iter 10 value 138.892304
## iter 20 value 120.715376
## iter 30 value 108.613577
## iter 40 value 106.993956
## iter 50 value 106.749270
## iter 60 value 106.720121
## final value 106.720007
## converged## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: decay## Loading required package: NeuralNetTools## Warning: replacing previous import by 'scales::alpha' when loading
## 'NeuralNetTools'
## a 80-1-1 network with 83 weights
## options were - entropy fitting decay=0.1
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1
## 1.60 0.25 -0.31 0.29 -0.43 -0.58 -0.33 -0.51 -0.20
## i9->h1 i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 i17->h1
## 0.67 -0.20 1.35 0.21 0.06 -0.33 1.02 -0.14 1.02
## i18->h1 i19->h1 i20->h1 i21->h1 i22->h1 i23->h1 i24->h1 i25->h1 i26->h1
## -0.29 -0.16 -0.46 -1.45 -0.06 0.26 0.31 -1.17 -0.23
## i27->h1 i28->h1 i29->h1 i30->h1 i31->h1 i32->h1 i33->h1 i34->h1 i35->h1
## -0.61 -1.60 0.64 -0.45 1.20 0.18 0.98 -0.83 2.30
## i36->h1 i37->h1 i38->h1 i39->h1 i40->h1 i41->h1 i42->h1 i43->h1 i44->h1
## 1.04 2.34 3.45 0.06 -0.63 1.29 -0.17 0.64 -0.13
## i45->h1 i46->h1 i47->h1 i48->h1 i49->h1 i50->h1 i51->h1 i52->h1 i53->h1
## 0.38 0.58 1.59 1.13 1.24 1.55 1.63 0.71 0.20
## i54->h1 i55->h1 i56->h1 i57->h1 i58->h1 i59->h1 i60->h1 i61->h1 i62->h1
## -0.34 0.22 -0.35 0.06 -0.36 -0.88 -0.66 -0.54 -0.88
## i63->h1 i64->h1 i65->h1 i66->h1 i67->h1 i68->h1 i69->h1 i70->h1 i71->h1
## 0.72 0.00 0.00 0.00 0.03 0.00 0.00 1.12 0.00
## i72->h1 i73->h1 i74->h1 i75->h1 i76->h1 i77->h1 i78->h1 i79->h1 i80->h1
## 0.00 -0.62 0.00 0.00 -0.50 -0.14 0.00 0.12 0.00
## b->o h1->o
## -2.57 7.81
## sold.fctr sold.fctr.predict.RFE.X.nnet.N sold.fctr.predict.RFE.X.nnet.Y
## 1 N 75 16
## 2 Y 16 296
## Prediction
## Reference N Y
## N 75 16
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.205955e-01 7.728938e-01 8.897522e-01 9.450553e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 4.291663e-15 1.000000e+00
## sold.fctr sold.fctr.predict.RFE.X.nnet.N sold.fctr.predict.RFE.X.nnet.Y
## 1 N 50 56
## 2 Y 16 310
## Prediction
## Reference N Y
## N 50 56
## Y 16 310
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.333333e-01 4.842817e-01 7.947758e-01 8.672487e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 4.917603e-05 4.302779e-06
## id
## 1 RFE.X.nnet
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 17.684 0.069
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8450092 0.7252747 0.9647436 0.9508841
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.9487179 0.8221854
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8897522 0.9450553 0.4849018
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7367751 0.5471698 0.9263804 0.8596336
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.8959538 0.8333333
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7947758 0.8672487 0.4842817
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02496325 0.0494171
## label step_major step_minor label_minor bgn end
## 9 fit.models_1_RFE.X 2 7 nnet 319.974 343.291
## 10 fit.models_1_RFE.X 2 8 avNNet 343.291 NA
## elapsed
## 9 23.317
## 10 NA
## [1] "fitting model: RFE.X.avNNet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting size = 1, decay = 0.1, bag = FALSE on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: decay## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: bag
## Length Class Mode
## model 5 -none- list
## repeats 1 -none- numeric
## bag 1 -none- logical
## names 80 -none- character
## terms 3 terms call
## coefnames 80 -none- character
## xlevels 0 -none- list
## xNames 80 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 2 -none- character
## sold.fctr sold.fctr.predict.RFE.X.avNNet.N
## 1 N 76
## 2 Y 15
## sold.fctr.predict.RFE.X.avNNet.Y
## 1 15
## 2 297
## Prediction
## Reference N Y
## N 76 15
## Y 15 297
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.255583e-01 7.870879e-01 8.954336e-01 9.492136e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 3.510143e-16 1.000000e+00
## sold.fctr sold.fctr.predict.RFE.X.avNNet.N
## 1 N 49
## 2 Y 14
## sold.fctr.predict.RFE.X.avNNet.Y
## 1 57
## 2 312
## Prediction
## Reference N Y
## N 49 57
## Y 14 312
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.356481e-01 4.858177e-01 7.972691e-01 8.693518e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 2.945937e-05 6.212772e-07
## id
## 1 RFE.X.avNNet
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 46.01 0.168
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8576007 0.7472527 0.9679487 0.9381692
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.9519231 0.8370616
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8954336 0.9492136 0.519995
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7163734 0.509434 0.9233129 0.8506048
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.8978417 0.8356481
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7972691 0.8693518 0.4858177
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01716327 0.02674727
## label step_major step_minor label_minor bgn end
## 10 fit.models_1_RFE.X 2 8 avNNet 343.291 393.098
## 11 fit.models_1_RFE.X 2 9 earth 393.099 NA
## elapsed
## 10 49.808
## 11 NA
## [1] "fitting model: RFE.X.earth"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr"## Loading required package: earth
## Loading required package: plotmo
## Loading required package: plotrix
##
## Attaching package: 'plotrix'
##
## The following object is masked from 'package:arm':
##
## rescale
##
## The following object is masked from 'package:gplots':
##
## plotCI
##
## Loading required package: TeachingDemos## Aggregating results
## Selecting tuning parameters
## Fitting nprune = 19, degree = 1 on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: degree
## Call: earth(x=matrix[403,80], y=factor.object, keepxy=TRUE,
## glm=list(family=function.object), degree=1, nprune=19)
##
## GLM coefficients
## Y
## (Intercept) 0.4827321
## cellular.fctrUnknown -1.0223877
## condition.fctrFor parts or not working -1.5724349
## prdl.descr.my.fctriPad3#0 1.4203941
## prdl.descr.my.fctriPadAir#0 2.9688891
## prdl.descr.my.fctriPadAir2#0 1.9123982
## prdl.descr.my.fctriPadAir2#1 20.1522633
## spdiff.cut.fctr(-100,-10] 0.8734681
## spdiff.cut.fctr(-10,-1] 1.9393898
## spdiff.cut.fctr(-1,0] 18.6697599
## spdiff.cut.fctr(0,1] 18.6989817
## spdiff.cut.fctr(1,10] 3.2684120
## spdiff.cut.fctr(10,100] 2.5081035
## spdiff.cut.fctr(100,1e+03] 19.7798996
## cellular.fctr1:carrier.fctrSprint 20.0036654
## h(0.693147-D.chrs.pnct11.n.log) 1.2092463
## h(D.terms.post.stem.n.log-2.83321) -25.7706424
## h(D.weight.post.stem.sum-4.70335) 1.4278276
## h(sprice.log10-4.09434) -1.9349924
##
## Earth selected 19 of 95 terms, and 18 of 80 predictors
## Termination condition: Reached nk 161
## Importance: spdiff.cut.fctr(10,100], spdiff.cut.fctr(100,1e+03], ...
## Number of terms at each degree of interaction: 1 18 (additive model)
## Earth GCV 0.1091402 RSS 36.27787 GRSq 0.3787867 RSq 0.4850669
##
## GLM null.deviance 430.5324 (402 dof) deviance 210.3658 (384 dof) iters 18
## sold.fctr sold.fctr.predict.RFE.X.earth.N
## 1 N 57
## 2 Y 11
## sold.fctr.predict.RFE.X.earth.Y
## 1 34
## 2 301
## Prediction
## Reference N Y
## N 57 34
## Y 11 301
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.883375e-01 6.492331e-01 8.534497e-01 9.173774e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.611762e-09 1.039668e-03
## sold.fctr sold.fctr.predict.RFE.X.earth.N
## 1 N 63
## 2 Y 28
## sold.fctr.predict.RFE.X.earth.Y
## 1 43
## 2 298
## Prediction
## Reference N Y
## N 63 43
## Y 28 298
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.356481e-01 5.339452e-01 7.972691e-01 8.693518e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 2.945937e-05 9.661421e-02
## id
## 1 RFE.X.earth
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 5.512 0.649
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8150183 0.6813187 0.9487179 0.9361968
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9304482 0.8204901
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8534497 0.9173774 0.4665753
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.695856 0.4622642 0.9294479 0.8513283
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.8935532 0.8356481
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7972691 0.8693518 0.5339452
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02390431 0.07977886
## label step_major step_minor label_minor bgn end
## 11 fit.models_1_RFE.X 2 9 earth 393.099 403.115
## 12 fit.models_1_All.X 3 0 setup 403.116 NA
## elapsed
## 11 10.016
## 12 NA
## label step_major step_minor label_minor bgn end
## 12 fit.models_1_All.X 3 0 setup 403.116 403.123
## 13 fit.models_1_All.X 3 1 glmnet 403.123 NA
## elapsed
## 12 0.007
## 13 NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.d20nexp,startprice.dcm2.is9,startprice.dcm1.is9,D.wrds.stop.n.log,D.chrs.pnct11.n.log,D.weight.post.stop.sum,D.chrs.pnct13.n.log,D.chrs.n.log,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,.rnorm,prdl.descr.my.fctr,startprice.dgt2.is9,storage.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,cellular.fctr,condition.fctr,startprice.dgt1.is9,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0196 on full training set 
## Length Class Mode
## a0 99 -none- numeric
## beta 8019 dgCMatrix S4
## df 99 -none- numeric
## dim 2 -none- numeric
## lambda 99 -none- numeric
## dev.ratio 99 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 81 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 3.64476130
## .rnorm
## -0.04715339
## D.chrs.pnct11.n.log
## -0.27178606
## D.wrds.stop.n.log
## 0.01060042
## cellular.fctrUnknown
## -0.52953985
## color.fctrGold
## 0.33200372
## color.fctrSpace Gray
## -0.09603546
## condition.fctrFor parts or not working
## -0.63433303
## condition.fctrSeller refurbished
## -0.98900984
## prdl.descr.my.fctriPad2#0
## 0.37476078
## prdl.descr.my.fctriPad3#0
## 0.52965983
## prdl.descr.my.fctriPad4#1
## -0.87493058
## prdl.descr.my.fctriPadAir#0
## 1.35391787
## prdl.descr.my.fctriPadAir#1
## 0.56987151
## prdl.descr.my.fctriPadAir2#0
## 0.81133923
## prdl.descr.my.fctriPadAir2#1
## 2.26536539
## prdl.descr.my.fctriPadmini#0
## -0.40026955
## prdl.descr.my.fctriPadmini#1
## -0.30746965
## prdl.descr.my.fctriPadmini2#0
## 0.24694801
## prdl.descr.my.fctriPadmini2#1
## -0.40192512
## prdl.descr.my.fctriPadmini3#0
## -0.48749579
## spdiff.cut.fctr(-100,-10]
## 0.09123465
## spdiff.cut.fctr(-10,-1]
## 0.69202823
## spdiff.cut.fctr(-1,0]
## 0.76441707
## spdiff.cut.fctr(0,1]
## 0.23611798
## spdiff.cut.fctr(1,10]
## 1.20160963
## spdiff.cut.fctr(10,100]
## 1.33218141
## spdiff.cut.fctr(100,1e+03]
## 2.03171518
## sprice.d20nexp
## 0.44484189
## sprice.log10
## -0.18966684
## sprice.root2
## -0.16335923
## startprice.dgt1.is9
## -0.29135197
## storage.fctr64
## -0.05396287
## cellular.fctr1:carrier.fctrSprint
## 1.70187130
## cellular.fctr1:carrier.fctrT-Mobile
## -0.37049649
## cellular.fctr1:carrier.fctrUnknown
## -0.03820746
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.52707167
## cellular.fctr1:carrier.fctrVerizon
## 0.15853089
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 3.677862404
## .rnorm
## -0.053647319
## D.chrs.pnct11.n.log
## -0.292500157
## D.wrds.stop.n.log
## 0.026964620
## cellular.fctr1
## 0.014741599
## cellular.fctrUnknown
## -0.534274721
## color.fctrGold
## 0.388246919
## color.fctrSpace Gray
## -0.136073978
## color.fctrUnknown
## -0.001838048
## condition.fctrFor parts or not working
## -0.682089508
## condition.fctrSeller refurbished
## -1.030680573
## prdl.descr.my.fctriPad2#0
## 0.400153344
## prdl.descr.my.fctriPad3#0
## 0.573981439
## prdl.descr.my.fctriPad4#1
## -0.878771328
## prdl.descr.my.fctriPadAir#0
## 1.461628835
## prdl.descr.my.fctriPadAir#1
## 0.632815937
## prdl.descr.my.fctriPadAir2#0
## 0.870119341
## prdl.descr.my.fctriPadAir2#1
## 2.379282285
## prdl.descr.my.fctriPadmini#0
## -0.399160497
## prdl.descr.my.fctriPadmini#1
## -0.326318251
## prdl.descr.my.fctriPadmini2#0
## 0.318996175
## prdl.descr.my.fctriPadmini2#1
## -0.411610833
## prdl.descr.my.fctriPadmini3#0
## -0.478878793
## spdiff.cut.fctr(-100,-10]
## 0.124043154
## spdiff.cut.fctr(-10,-1]
## 0.740186901
## spdiff.cut.fctr(-1,0]
## 0.839914557
## spdiff.cut.fctr(0,1]
## 0.308327034
## spdiff.cut.fctr(1,10]
## 1.269856795
## spdiff.cut.fctr(10,100]
## 1.388885563
## spdiff.cut.fctr(100,1e+03]
## 2.147602530
## sprice.d20nexp
## 0.445759244
## sprice.log10
## -0.193783186
## sprice.root2
## -0.167302133
## startprice.dgt1.is9
## -0.300020502
## storage.fctr64
## -0.072181044
## cellular.fctr1:carrier.fctrSprint
## 1.834493622
## cellular.fctr1:carrier.fctrT-Mobile
## -0.479004489
## cellular.fctr1:carrier.fctrUnknown
## -0.047367063
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.531445887
## cellular.fctr1:carrier.fctrVerizon
## 0.161705067
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 60
## 2 Y 12
## sold.fctr.predict.All.X.glmnet.Y
## 1 31
## 2 300
## Prediction
## Reference N Y
## N 60 31
## Y 12 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.933002e-01 6.704574e-01 8.589742e-01 9.216980e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 4.540937e-10 6.051564e-03
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 43
## 2 Y 14
## sold.fctr.predict.All.X.glmnet.Y
## 1 63
## 2 312
## Prediction
## Reference N Y
## N 43 63
## Y 14 312
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.217593e-01 4.297470e-01 7.823447e-01 8.566975e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 5.040175e-04 4.497623e-08
## id
## 1 All.X.glmnet
## feats
## 1 spdiff.cut.fctr,sprice.d20nexp,startprice.dcm2.is9,startprice.dcm1.is9,D.wrds.stop.n.log,D.chrs.pnct11.n.log,D.weight.post.stop.sum,D.chrs.pnct13.n.log,D.chrs.n.log,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,.rnorm,prdl.descr.my.fctr,startprice.dgt2.is9,storage.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,cellular.fctr,condition.fctr,startprice.dgt1.is9,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 11.183 0.71
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8104396 0.6593407 0.9615385 0.9328684
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.933126 0.8445059
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8589742 0.921698 0.5106639
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6813578 0.4056604 0.9570552 0.8419956
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.8901569 0.8217593
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7823447 0.8566975 0.429747
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0179705 0.05239318
## label step_major step_minor label_minor bgn
## 13 fit.models_1_All.X 3 1 glmnet 403.123
## 14 fit.models_1_Best.Interact 4 0 setup 418.177
## end elapsed
## 13 418.177 15.054
## 14 NA NA
## label step_major step_minor
## 14 fit.models_1_Best.Interact 4 0
## 15 fit.models_1_Max.cor.Y.rcv.3X5.Interact 4 1
## label_minor bgn end elapsed
## 14 setup 418.177 418.204 0.027
## 15 glmnet 418.205 NA NA
## [1] "fitting model: Max.cor.Y.rcv.3X5.Interact.glmnet"
## [1] " indep_vars: sprice.root2,spdiff.cut.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000911 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8441854 1.2181523
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1400769 4.9534292
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.7772971 3.4510453
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9607768 6.2329383
## sprice.root2
## -0.1099784
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8287020 1.2267835
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1514437 5.0427690
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.8667913 3.4716512
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9718351 6.3173344
## sprice.root2
## -0.1093736
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.Interact.glmnet.N
## 1 N 49
## 2 Y 16
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Interact.glmnet.Y
## 1 42
## 2 296
## Prediction
## Reference N Y
## N 49 42
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.560794e-01 5.420275e-01 8.179561e-01 8.888671e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.460262e-05 1.028323e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.Interact.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Interact.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats
## 1 Max.cor.Y.rcv.3X5.Interact.glmnet sprice.root2,spdiff.cut.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.144 0.02
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7481685 0.5604396 0.9358974 0.8976648
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9107692 0.8528223
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8179561 0.8888671 0.5488242
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7486978 0.5188679 0.9785276 0.8747106
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.9166667 0.8657407
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8299202 0.8964488 0.5784089
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02087114 0.05769317# Check if other preProcess methods improve model performance
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse=".")
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glb_tune_models_df,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
} ## [1] "fitting model: Max.cor.Y.rcv.3X5.YeoJohnson.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.00404 on full training set
## Length Class Mode
## a0 94 -none- numeric
## beta 752 dgCMatrix S4
## df 94 -none- numeric
## dim 2 -none- numeric
## lambda 94 -none- numeric
## dev.ratio 94 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 2.0990887 0.9454776
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 1.7369023 3.0916106
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 2.6199616 2.7279413
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.5764264 4.3626180
## sprice.root2
## -0.4254551
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 2.0479118 0.9736191
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 1.7739805 3.2096923
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 2.7439863 2.7890274
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.6125168 4.4683475
## sprice.root2
## -0.4208425
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.YeoJohnson.glmnet.N
## 1 N 46
## 2 Y 15
## sold.fctr.predict.Max.cor.Y.rcv.3X5.YeoJohnson.glmnet.Y
## 1 45
## 2 297
## Prediction
## Reference N Y
## N 46 45
## Y 15 297
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.511166e-01 5.178849e-01 8.125512e-01 8.844240e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 7.384632e-05 1.811974e-04
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.YeoJohnson.glmnet.N
## 1 N 55
## 2 Y 8
## sold.fctr.predict.Max.cor.Y.rcv.3X5.YeoJohnson.glmnet.Y
## 1 51
## 2 318
## Prediction
## Reference N Y
## N 55 51
## Y 8 318
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.634259e-01 5.727218e-01 8.273915e-01 8.943819e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 1.673557e-08 4.553107e-08
## id feats
## 1 Max.cor.Y.rcv.3X5.YeoJohnson.glmnet spdiff.cut.fctr,sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.448 0.04
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.753663 0.5714286 0.9358974 0.8967843
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9082569 0.8528223
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8125512 0.884424 0.5516038
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.747164 0.5188679 0.9754601 0.8712669
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.9151079 0.8634259
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8273915 0.8943819 0.5727218
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0215279 0.05758805
## [1] "fitting model: Max.cor.Y.rcv.3X5.center.scale.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000911 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha 
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 2.2495956 0.5176941
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 0.6615296 1.7155333
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 1.0866521 1.0557555
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 1.1537393 0.6186482
## sprice.root2
## -0.7152591
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 2.2653765 0.5213622
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 0.6650433 1.7464746
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 1.1070086 1.0620593
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 1.1580484 0.6270249
## sprice.root2
## -0.7113259
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.center.scale.glmnet.N
## 1 N 49
## 2 Y 16
## sold.fctr.predict.Max.cor.Y.rcv.3X5.center.scale.glmnet.Y
## 1 42
## 2 296
## Prediction
## Reference N Y
## N 49 42
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.560794e-01 5.420275e-01 8.179561e-01 8.888671e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.460262e-05 1.028323e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.center.scale.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.3X5.center.scale.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats
## 1 Max.cor.Y.rcv.3X5.center.scale.glmnet spdiff.cut.fctr,sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.263 0.025
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7481685 0.5604396 0.9358974 0.8976648
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9107692 0.8528223
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8179561 0.8888671 0.5488242
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7486978 0.5188679 0.9785276 0.8747106
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.9166667 0.8657407
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8299202 0.8964488 0.5784089
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02087114 0.05769317
## [1] "fitting model: Max.cor.Y.rcv.3X5.range.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000911 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha 
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8331875 1.2181523
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1400769 4.9534292
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.7772971 3.4510453
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9607768 6.2329383
## sprice.root2
## -3.0996608
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-100,-10]
## 0.8177647 1.2267835
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-1,0]
## 2.1514437 5.0427690
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(1,10]
## 4.8667913 3.4716512
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(100,1e+03]
## 2.9718351 6.3173344
## sprice.root2
## -3.0826156
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.range.glmnet.N
## 1 N 49
## 2 Y 16
## sold.fctr.predict.Max.cor.Y.rcv.3X5.range.glmnet.Y
## 1 42
## 2 296
## Prediction
## Reference N Y
## N 49 42
## Y 16 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.560794e-01 5.420275e-01 8.179561e-01 8.888671e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 2.460262e-05 1.028323e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.range.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.3X5.range.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats
## 1 Max.cor.Y.rcv.3X5.range.glmnet spdiff.cut.fctr,sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.247 0.024
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7481685 0.5604396 0.9358974 0.8976648
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9107692 0.8528223
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8179561 0.8888671 0.5488242
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7486978 0.5188679 0.9785276 0.8747106
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.9166667 0.8657407
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8299202 0.8964488 0.5784089
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02087114 0.05769317
## [1] "fitting model: Max.cor.Y.rcv.3X5.pca.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000751 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 83 -none- numeric
## beta 581 dgCMatrix S4
## df 83 -none- numeric
## dim 2 -none- numeric
## lambda 83 -none- numeric
## dev.ratio 83 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 7 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) PC1 PC2 PC3 PC4 PC5
## 1.91009272 -1.06727166 0.38691182 0.08841547 0.14140777 0.03887733
## PC6 PC7
## 0.27744587 1.34137518
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "PC1" "PC2" "PC3" "PC4"
## [6] "PC5" "PC6" "PC7"
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.pca.glmnet.N
## 1 N 47
## 2 Y 23
## sold.fctr.predict.Max.cor.Y.rcv.3X5.pca.glmnet.Y
## 1 44
## 2 289
## Prediction
## Reference N Y
## N 47 44
## Y 23 289
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.833746898 0.482174021 0.793733671 0.868771672 0.774193548
## AccuracyPValue McnemarPValue
## 0.001950422 0.014550049
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.pca.glmnet.N
## 1 N 56
## 2 Y 28
## sold.fctr.predict.Max.cor.Y.rcv.3X5.pca.glmnet.Y
## 1 50
## 2 298
## Prediction
## Reference N Y
## N 56 50
## Y 28 298
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8194444444 0.4757281553 0.7798652661 0.8545804176 0.7546296296
## AccuracyPValue McnemarPValue
## 0.0007670289 0.0174171303
## id feats
## 1 Max.cor.Y.rcv.3X5.pca.glmnet spdiff.cut.fctr,sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.17 0.021
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7213828 0.5164835 0.9262821 0.8813222
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.896124 0.8263313
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7937337 0.8687717 0.4556733
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6610719 0.3773585 0.9447853 0.8420679
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.884273 0.8194444
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7798653 0.8545804 0.4757282
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02008542 0.06116888
## [1] "fitting model: Max.cor.Y.rcv.3X5.ica.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000576 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 84 -none- numeric
## beta 252 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 3 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) ICA1 ICA2 ICA3
## 1.9498302 0.4121608 -1.2592082 -1.3071106
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "ICA1" "ICA2" "ICA3"
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.ica.glmnet.Y
## 1 N 91
## 2 Y 312
## Prediction
## Reference N Y
## N 0 91
## Y 0 312
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.741935e-01 0.000000e+00 7.301947e-01 8.141127e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 5.280696e-01 3.926255e-21
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.ica.glmnet.Y
## 1 N 106
## 2 Y 326
## Prediction
## Reference N Y
## N 0 106
## Y 0 326
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.546296e-01 0.000000e+00 7.112313e-01 7.945009e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 5.260575e-01 2.013550e-24
## id feats
## 1 Max.cor.Y.rcv.3X5.ica.glmnet spdiff.cut.fctr,sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.455 0.021
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5975275 0.2527473 0.9423077 0.8153881
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.1 0.8727273 0.779172
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7301947 0.8141127 0.2083131
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5229772 0.1226415 0.9233129 0.7595642
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.8601583 0.7546296
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7112313 0.7945009 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01509475 0.03918023
## [1] "fitting model: Max.cor.Y.rcv.3X5.spatialSign.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.102 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 94 -none- numeric
## beta 752 dgCMatrix S4
## df 94 -none- numeric
## dim 2 -none- numeric
## lambda 94 -none- numeric
## dev.ratio 94 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-10,-1]
## 1.7114273 0.3132773
## spdiff.cut.fctr(-1,0] spdiff.cut.fctr(0,1]
## 0.7097893 0.6442696
## spdiff.cut.fctr(1,10] spdiff.cut.fctr(10,100]
## 0.6539306 0.7299291
## spdiff.cut.fctr(100,1e+03] sprice.root2
## 0.7648223 -1.6222599
## [1] "max lambda < lambdaOpt:"
## (Intercept) spdiff.cut.fctr(-10,-1]
## 1.7403220 0.3242987
## spdiff.cut.fctr(-1,0] spdiff.cut.fctr(0,1]
## 0.7436561 0.6788564
## spdiff.cut.fctr(1,10] spdiff.cut.fctr(10,100]
## 0.6845602 0.7631793
## spdiff.cut.fctr(100,1e+03] sprice.root2
## 0.8179521 -1.6755633
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.spatialSign.glmnet.N
## 1 N 49
## 2 Y 15
## sold.fctr.predict.Max.cor.Y.rcv.3X5.spatialSign.glmnet.Y
## 1 42
## 2 297
## Prediction
## Reference N Y
## N 49 42
## Y 15 297
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.585608e-01 5.479662e-01 8.206637e-01 8.910835e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 1.379763e-05 5.736335e-04
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.spatialSign.glmnet.N
## 1 N 55
## 2 Y 7
## sold.fctr.predict.Max.cor.Y.rcv.3X5.spatialSign.glmnet.Y
## 1 51
## 2 319
## Prediction
## Reference N Y
## N 55 51
## Y 7 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.657407e-01 5.784089e-01 8.299202e-01 8.964488e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 7.979833e-09 1.640553e-08
## id feats
## 1 Max.cor.Y.rcv.3X5.spatialSign.glmnet spdiff.cut.fctr,sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.306 0.03
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7451923 0.5384615 0.9519231 0.8920999
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9124424 0.8586021
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8206637 0.8910835 0.5466577
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7486978 0.5188679 0.9785276 0.8697911
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.9166667 0.8657407
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8299202 0.8964488 0.5784089
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01663876 0.05469216 # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.chrs.uppr.n.log, glb_allobs_df$A.chrs.uppr.n.log)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# mydsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glbFeatsExclude)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet Low.cor.X.glmnet
## RFE.X.glm RFE.X.glm
## RFE.X.bayesglm RFE.X.bayesglm
## RFE.X.glmnet RFE.X.glmnet
## RFE.X.rpart RFE.X.rpart
## RFE.X.gbm RFE.X.gbm
## RFE.X.rf RFE.X.rf
## RFE.X.nnet RFE.X.nnet
## RFE.X.avNNet RFE.X.avNNet
## RFE.X.earth RFE.X.earth
## All.X.glmnet All.X.glmnet
## Max.cor.Y.rcv.3X5.Interact.glmnet Max.cor.Y.rcv.3X5.Interact.glmnet
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet Max.cor.Y.rcv.3X5.YeoJohnson.glmnet
## Max.cor.Y.rcv.3X5.center.scale.glmnet Max.cor.Y.rcv.3X5.center.scale.glmnet
## Max.cor.Y.rcv.3X5.range.glmnet Max.cor.Y.rcv.3X5.range.glmnet
## Max.cor.Y.rcv.3X5.pca.glmnet Max.cor.Y.rcv.3X5.pca.glmnet
## Max.cor.Y.rcv.3X5.ica.glmnet Max.cor.Y.rcv.3X5.ica.glmnet
## Max.cor.Y.rcv.3X5.spatialSign.glmnet Max.cor.Y.rcv.3X5.spatialSign.glmnet
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X1.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X3.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.5X1.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.5X3.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.5X5.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.1X1.cp.0.rpart spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rpart spdiff.cut.fctr,sprice.root2
## Interact.High.cor.Y.glmnet spdiff.cut.fctr,sprice.root2,spdiff.cut.fctr:sprice.log10,spdiff.cut.fctr:startprice.dcm2.is9,spdiff.cut.fctr:cellular.fctr,spdiff.cut.fctr:sprice.root2
## Low.cor.X.glmnet spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.chrs.pnct11.n.log,D.weight.post.stop.sum,D.chrs.pnct13.n.log,D.chrs.n.log,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,.rnorm,prdl.descr.my.fctr,startprice.dgt2.is9,storage.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,cellular.fctr,condition.fctr,startprice.dgt1.is9,sprice.root2
## RFE.X.glm spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## RFE.X.bayesglm spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## RFE.X.glmnet spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## RFE.X.rpart spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.gbm spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.rf spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.nnet spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.avNNet spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.earth spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## All.X.glmnet spdiff.cut.fctr,sprice.d20nexp,startprice.dcm2.is9,startprice.dcm1.is9,D.wrds.stop.n.log,D.chrs.pnct11.n.log,D.weight.post.stop.sum,D.chrs.pnct13.n.log,D.chrs.n.log,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,.rnorm,prdl.descr.my.fctr,startprice.dgt2.is9,storage.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,cellular.fctr,condition.fctr,startprice.dgt1.is9,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr
## Max.cor.Y.rcv.3X5.Interact.glmnet sprice.root2,spdiff.cut.fctr
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.center.scale.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.range.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.pca.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.ica.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.spatialSign.glmnet spdiff.cut.fctr,sprice.root2
## max.nTuningRuns
## MFO.myMFO_classfr 0
## Random.myrandom_classfr 0
## Max.cor.Y.rcv.1X1.glmnet 0
## Max.cor.Y.rcv.3X1.glmnet 25
## Max.cor.Y.rcv.3X3.glmnet 25
## Max.cor.Y.rcv.3X5.glmnet 25
## Max.cor.Y.rcv.5X1.glmnet 25
## Max.cor.Y.rcv.5X3.glmnet 25
## Max.cor.Y.rcv.5X5.glmnet 25
## Max.cor.Y.rcv.1X1.cp.0.rpart 0
## Max.cor.Y.rpart 5
## Interact.High.cor.Y.glmnet 25
## Low.cor.X.glmnet 25
## RFE.X.glm 1
## RFE.X.bayesglm 1
## RFE.X.glmnet 25
## RFE.X.rpart 5
## RFE.X.gbm 25
## RFE.X.rf 5
## RFE.X.nnet 25
## RFE.X.avNNet 25
## RFE.X.earth 5
## All.X.glmnet 25
## Max.cor.Y.rcv.3X5.Interact.glmnet 25
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 25
## Max.cor.Y.rcv.3X5.center.scale.glmnet 25
## Max.cor.Y.rcv.3X5.range.glmnet 25
## Max.cor.Y.rcv.3X5.pca.glmnet 25
## Max.cor.Y.rcv.3X5.ica.glmnet 25
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 25
## min.elapsedtime.everything
## MFO.myMFO_classfr 0.293
## Random.myrandom_classfr 0.274
## Max.cor.Y.rcv.1X1.glmnet 0.762
## Max.cor.Y.rcv.3X1.glmnet 1.408
## Max.cor.Y.rcv.3X3.glmnet 2.021
## Max.cor.Y.rcv.3X5.glmnet 2.559
## Max.cor.Y.rcv.5X1.glmnet 1.638
## Max.cor.Y.rcv.5X3.glmnet 2.396
## Max.cor.Y.rcv.5X5.glmnet 3.706
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.731
## Max.cor.Y.rpart 1.503
## Interact.High.cor.Y.glmnet 4.685
## Low.cor.X.glmnet 10.304
## RFE.X.glm 1.925
## RFE.X.bayesglm 3.193
## RFE.X.glmnet 11.142
## RFE.X.rpart 2.462
## RFE.X.gbm 5.096
## RFE.X.rf 3.890
## RFE.X.nnet 17.684
## RFE.X.avNNet 46.010
## RFE.X.earth 5.512
## All.X.glmnet 11.183
## Max.cor.Y.rcv.3X5.Interact.glmnet 2.144
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 2.448
## Max.cor.Y.rcv.3X5.center.scale.glmnet 2.263
## Max.cor.Y.rcv.3X5.range.glmnet 2.247
## Max.cor.Y.rcv.3X5.pca.glmnet 2.170
## Max.cor.Y.rcv.3X5.ica.glmnet 2.455
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 2.306
## min.elapsedtime.final
## MFO.myMFO_classfr 0.003
## Random.myrandom_classfr 0.001
## Max.cor.Y.rcv.1X1.glmnet 0.030
## Max.cor.Y.rcv.3X1.glmnet 0.019
## Max.cor.Y.rcv.3X3.glmnet 0.020
## Max.cor.Y.rcv.3X5.glmnet 0.021
## Max.cor.Y.rcv.5X1.glmnet 0.019
## Max.cor.Y.rcv.5X3.glmnet 0.019
## Max.cor.Y.rcv.5X5.glmnet 0.022
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.013
## Max.cor.Y.rpart 0.012
## Interact.High.cor.Y.glmnet 0.439
## Low.cor.X.glmnet 0.462
## RFE.X.glm 0.085
## RFE.X.bayesglm 0.169
## RFE.X.glmnet 0.713
## RFE.X.rpart 0.039
## RFE.X.gbm 0.206
## RFE.X.rf 1.417
## RFE.X.nnet 0.069
## RFE.X.avNNet 0.168
## RFE.X.earth 0.649
## All.X.glmnet 0.710
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.020
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.040
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.025
## Max.cor.Y.rcv.3X5.range.glmnet 0.024
## Max.cor.Y.rcv.3X5.pca.glmnet 0.021
## Max.cor.Y.rcv.3X5.ica.glmnet 0.021
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.030
## max.AUCpROC.fit max.Sens.fit
## MFO.myMFO_classfr 0.5000000 0.0000000
## Random.myrandom_classfr 0.5054945 0.2417582
## Max.cor.Y.rcv.1X1.glmnet 0.7536630 0.5714286
## Max.cor.Y.rcv.3X1.glmnet 0.7481685 0.5604396
## Max.cor.Y.rcv.3X3.glmnet 0.7481685 0.5604396
## Max.cor.Y.rcv.3X5.glmnet 0.7481685 0.5604396
## Max.cor.Y.rcv.5X1.glmnet 0.7536630 0.5714286
## Max.cor.Y.rcv.5X3.glmnet 0.7481685 0.5604396
## Max.cor.Y.rcv.5X5.glmnet 0.7481685 0.5604396
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7685440 0.6043956
## Max.cor.Y.rpart 0.8031136 0.7472527
## Interact.High.cor.Y.glmnet 0.7639652 0.5824176
## Low.cor.X.glmnet 0.7939560 0.6263736
## RFE.X.glm 0.8472985 0.7362637
## RFE.X.bayesglm 0.8269231 0.6923077
## RFE.X.glmnet 0.8104396 0.6593407
## RFE.X.rpart 0.8031136 0.7472527
## RFE.X.gbm 0.8191392 0.6703297
## RFE.X.rf 0.9983974 1.0000000
## RFE.X.nnet 0.8450092 0.7252747
## RFE.X.avNNet 0.8576007 0.7472527
## RFE.X.earth 0.8150183 0.6813187
## All.X.glmnet 0.8104396 0.6593407
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.7481685 0.5604396
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.7536630 0.5714286
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.7481685 0.5604396
## Max.cor.Y.rcv.3X5.range.glmnet 0.7481685 0.5604396
## Max.cor.Y.rcv.3X5.pca.glmnet 0.7213828 0.5164835
## Max.cor.Y.rcv.3X5.ica.glmnet 0.5975275 0.2527473
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.7451923 0.5384615
## max.Spec.fit max.AUCROCR.fit
## MFO.myMFO_classfr 1.0000000 0.5000000
## Random.myrandom_classfr 0.7692308 0.5112179
## Max.cor.Y.rcv.1X1.glmnet 0.9358974 0.8966434
## Max.cor.Y.rcv.3X1.glmnet 0.9358974 0.8976648
## Max.cor.Y.rcv.3X3.glmnet 0.9358974 0.8976648
## Max.cor.Y.rcv.3X5.glmnet 0.9358974 0.8979466
## Max.cor.Y.rcv.5X1.glmnet 0.9358974 0.8966434
## Max.cor.Y.rcv.5X3.glmnet 0.9358974 0.8976648
## Max.cor.Y.rcv.5X5.glmnet 0.9358974 0.8979114
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.9326923 0.8748239
## Max.cor.Y.rpart 0.8589744 0.8036419
## Interact.High.cor.Y.glmnet 0.9455128 0.8969956
## Low.cor.X.glmnet 0.9615385 0.9310721
## RFE.X.glm 0.9583333 0.9598126
## RFE.X.bayesglm 0.9615385 0.9455128
## RFE.X.glmnet 0.9615385 0.9328684
## RFE.X.rpart 0.8589744 0.8036419
## RFE.X.gbm 0.9679487 0.9553571
## RFE.X.rf 0.9967949 0.9999824
## RFE.X.nnet 0.9647436 0.9508841
## RFE.X.avNNet 0.9679487 0.9381692
## RFE.X.earth 0.9487179 0.9361968
## All.X.glmnet 0.9615385 0.9328684
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.9358974 0.8976648
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.9358974 0.8967843
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.9358974 0.8976648
## Max.cor.Y.rcv.3X5.range.glmnet 0.9358974 0.8976648
## Max.cor.Y.rcv.3X5.pca.glmnet 0.9262821 0.8813222
## Max.cor.Y.rcv.3X5.ica.glmnet 0.9423077 0.8153881
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.9519231 0.8920999
## opt.prob.threshold.fit
## MFO.myMFO_classfr 0.2
## Random.myrandom_classfr 0.2
## Max.cor.Y.rcv.1X1.glmnet 0.4
## Max.cor.Y.rcv.3X1.glmnet 0.4
## Max.cor.Y.rcv.3X3.glmnet 0.4
## Max.cor.Y.rcv.3X5.glmnet 0.4
## Max.cor.Y.rcv.5X1.glmnet 0.4
## Max.cor.Y.rcv.5X3.glmnet 0.4
## Max.cor.Y.rcv.5X5.glmnet 0.4
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.5
## Max.cor.Y.rpart 0.9
## Interact.High.cor.Y.glmnet 0.5
## Low.cor.X.glmnet 0.5
## RFE.X.glm 0.5
## RFE.X.bayesglm 0.5
## RFE.X.glmnet 0.5
## RFE.X.rpart 0.9
## RFE.X.gbm 0.5
## RFE.X.rf 0.6
## RFE.X.nnet 0.6
## RFE.X.avNNet 0.6
## RFE.X.earth 0.4
## All.X.glmnet 0.5
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.4
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.4
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.4
## Max.cor.Y.rcv.3X5.range.glmnet 0.4
## Max.cor.Y.rcv.3X5.pca.glmnet 0.5
## Max.cor.Y.rcv.3X5.ica.glmnet 0.1
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5
## max.f.score.fit max.Accuracy.fit
## MFO.myMFO_classfr 0.8727273 0.7741935
## Random.myrandom_classfr 0.8727273 0.7741935
## Max.cor.Y.rcv.1X1.glmnet 0.9082569 0.8511166
## Max.cor.Y.rcv.3X1.glmnet 0.9107692 0.8561083
## Max.cor.Y.rcv.3X3.glmnet 0.9107692 0.8528223
## Max.cor.Y.rcv.3X5.glmnet 0.9121726 0.8531306
## Max.cor.Y.rcv.5X1.glmnet 0.9082569 0.8510426
## Max.cor.Y.rcv.5X3.glmnet 0.9107692 0.8536089
## Max.cor.Y.rcv.5X5.glmnet 0.9121726 0.8541198
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.9107981 0.8585608
## Max.cor.Y.rpart 0.8888889 0.8156317
## Interact.High.cor.Y.glmnet 0.9147287 0.8544868
## Low.cor.X.glmnet 0.9287926 0.8412075
## RFE.X.glm 0.9417323 0.7824765
## RFE.X.bayesglm 0.9375000 0.8337694
## RFE.X.glmnet 0.9331260 0.8445059
## RFE.X.rpart 0.8888889 0.8147595
## RFE.X.gbm 0.9378882 0.8221792
## RFE.X.rf 0.9983949 0.8188586
## RFE.X.nnet 0.9487179 0.8221854
## RFE.X.avNNet 0.9519231 0.8370616
## RFE.X.earth 0.9304482 0.8204901
## All.X.glmnet 0.9331260 0.8445059
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.9107692 0.8528223
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.9082569 0.8528223
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.9107692 0.8528223
## Max.cor.Y.rcv.3X5.range.glmnet 0.9107692 0.8528223
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8961240 0.8263313
## Max.cor.Y.rcv.3X5.ica.glmnet 0.8727273 0.7791720
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.9124424 0.8586021
## max.AccuracyLower.fit
## MFO.myMFO_classfr 0.7301947
## Random.myrandom_classfr 0.7301947
## Max.cor.Y.rcv.1X1.glmnet 0.8125512
## Max.cor.Y.rcv.3X1.glmnet 0.8179561
## Max.cor.Y.rcv.3X3.glmnet 0.8179561
## Max.cor.Y.rcv.3X5.glmnet 0.8206637
## Max.cor.Y.rcv.5X1.glmnet 0.8125512
## Max.cor.Y.rcv.5X3.glmnet 0.8179561
## Max.cor.Y.rcv.5X5.glmnet 0.8206637
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8206637
## Max.cor.Y.rpart 0.7937337
## Interact.High.cor.Y.glmnet 0.8260892
## Low.cor.X.glmnet 0.8506945
## RFE.X.glm 0.8756722
## RFE.X.bayesglm 0.8672986
## RFE.X.glmnet 0.8589742
## RFE.X.rpart 0.7937337
## RFE.X.gbm 0.8672986
## RFE.X.rf 0.9862527
## RFE.X.nnet 0.8897522
## RFE.X.avNNet 0.8954336
## RFE.X.earth 0.8534497
## All.X.glmnet 0.8589742
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.8179561
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8125512
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8179561
## Max.cor.Y.rcv.3X5.range.glmnet 0.8179561
## Max.cor.Y.rcv.3X5.pca.glmnet 0.7937337
## Max.cor.Y.rcv.3X5.ica.glmnet 0.7301947
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8206637
## max.AccuracyUpper.fit max.Kappa.fit
## MFO.myMFO_classfr 0.8141127 0.0000000
## Random.myrandom_classfr 0.8141127 0.0000000
## Max.cor.Y.rcv.1X1.glmnet 0.8844240 0.5178849
## Max.cor.Y.rcv.3X1.glmnet 0.8888671 0.5565229
## Max.cor.Y.rcv.3X3.glmnet 0.8888671 0.5488242
## Max.cor.Y.rcv.3X5.glmnet 0.8910835 0.5466137
## Max.cor.Y.rcv.5X1.glmnet 0.8844240 0.5425612
## Max.cor.Y.rcv.5X3.glmnet 0.8888671 0.5444976
## Max.cor.Y.rcv.5X5.glmnet 0.8910835 0.5454265
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8910835 0.5703866
## Max.cor.Y.rpart 0.8687717 0.4861128
## Interact.High.cor.Y.glmnet 0.8955055 0.5516326
## Low.cor.X.glmnet 0.9152098 0.4975288
## RFE.X.glm 0.9345315 0.3926215
## RFE.X.bayesglm 0.9281401 0.5153729
## RFE.X.glmnet 0.9216980 0.5106639
## RFE.X.rpart 0.8687717 0.4431850
## RFE.X.gbm 0.9281401 0.4498412
## RFE.X.rf 0.9999372 0.4631079
## RFE.X.nnet 0.9450553 0.4849018
## RFE.X.avNNet 0.9492136 0.5199950
## RFE.X.earth 0.9173774 0.4665753
## All.X.glmnet 0.9216980 0.5106639
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.8888671 0.5488242
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8844240 0.5516038
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8888671 0.5488242
## Max.cor.Y.rcv.3X5.range.glmnet 0.8888671 0.5488242
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8687717 0.4556733
## Max.cor.Y.rcv.3X5.ica.glmnet 0.8141127 0.2083131
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8910835 0.5466577
## max.AUCpROC.OOB max.Sens.OOB
## MFO.myMFO_classfr 0.5000000 0.0000000
## Random.myrandom_classfr 0.5212988 0.2358491
## Max.cor.Y.rcv.1X1.glmnet 0.7471640 0.5188679
## Max.cor.Y.rcv.3X1.glmnet 0.7486978 0.5188679
## Max.cor.Y.rcv.3X3.glmnet 0.7486978 0.5188679
## Max.cor.Y.rcv.3X5.glmnet 0.7486978 0.5188679
## Max.cor.Y.rcv.5X1.glmnet 0.7471640 0.5188679
## Max.cor.Y.rcv.5X3.glmnet 0.7486978 0.5188679
## Max.cor.Y.rcv.5X5.glmnet 0.7486978 0.5188679
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.6697824 0.4622642
## Max.cor.Y.rpart 0.7600995 0.6981132
## Interact.High.cor.Y.glmnet 0.7205116 0.4716981
## Low.cor.X.glmnet 0.6734576 0.3867925
## RFE.X.glm 0.7009202 0.5000000
## RFE.X.bayesglm 0.7304086 0.5283019
## RFE.X.glmnet 0.6813578 0.4056604
## RFE.X.rpart 0.7600995 0.6981132
## RFE.X.gbm 0.6785218 0.4245283
## RFE.X.rf 0.7465563 0.5943396
## RFE.X.nnet 0.7367751 0.5471698
## RFE.X.avNNet 0.7163734 0.5094340
## RFE.X.earth 0.6958560 0.4622642
## All.X.glmnet 0.6813578 0.4056604
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.7486978 0.5188679
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.7471640 0.5188679
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.7486978 0.5188679
## Max.cor.Y.rcv.3X5.range.glmnet 0.7486978 0.5188679
## Max.cor.Y.rcv.3X5.pca.glmnet 0.6610719 0.3773585
## Max.cor.Y.rcv.3X5.ica.glmnet 0.5229772 0.1226415
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.7486978 0.5188679
## max.Spec.OOB max.AUCROCR.OOB
## MFO.myMFO_classfr 1.0000000 0.5000000
## Random.myrandom_classfr 0.8067485 0.5310800
## Max.cor.Y.rcv.1X1.glmnet 0.9754601 0.8730032
## Max.cor.Y.rcv.3X1.glmnet 0.9785276 0.8747106
## Max.cor.Y.rcv.3X3.glmnet 0.9785276 0.8747106
## Max.cor.Y.rcv.3X5.glmnet 0.9785276 0.8749132
## Max.cor.Y.rcv.5X1.glmnet 0.9754601 0.8730032
## Max.cor.Y.rcv.5X3.glmnet 0.9785276 0.8747106
## Max.cor.Y.rcv.5X5.glmnet 0.9785276 0.8747974
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8773006 0.8205087
## Max.cor.Y.rpart 0.8220859 0.7542540
## Interact.High.cor.Y.glmnet 0.9693252 0.8736399
## Low.cor.X.glmnet 0.9601227 0.8467126
## RFE.X.glm 0.9018405 0.8206390
## RFE.X.bayesglm 0.9325153 0.8568411
## RFE.X.glmnet 0.9570552 0.8419956
## RFE.X.rpart 0.8220859 0.7542540
## RFE.X.gbm 0.9325153 0.8382625
## RFE.X.rf 0.8987730 0.8557414
## RFE.X.nnet 0.9263804 0.8596336
## RFE.X.avNNet 0.9233129 0.8506048
## RFE.X.earth 0.9294479 0.8513283
## All.X.glmnet 0.9570552 0.8419956
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.9785276 0.8747106
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.9754601 0.8712669
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.9785276 0.8747106
## Max.cor.Y.rcv.3X5.range.glmnet 0.9785276 0.8747106
## Max.cor.Y.rcv.3X5.pca.glmnet 0.9447853 0.8420679
## Max.cor.Y.rcv.3X5.ica.glmnet 0.9233129 0.7595642
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.9785276 0.8697911
## opt.prob.threshold.OOB
## MFO.myMFO_classfr 0.2
## Random.myrandom_classfr 0.2
## Max.cor.Y.rcv.1X1.glmnet 0.5
## Max.cor.Y.rcv.3X1.glmnet 0.5
## Max.cor.Y.rcv.3X3.glmnet 0.5
## Max.cor.Y.rcv.3X5.glmnet 0.5
## Max.cor.Y.rcv.5X1.glmnet 0.5
## Max.cor.Y.rcv.5X3.glmnet 0.5
## Max.cor.Y.rcv.5X5.glmnet 0.5
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.2
## Max.cor.Y.rpart 0.3
## Interact.High.cor.Y.glmnet 0.6
## Low.cor.X.glmnet 0.5
## RFE.X.glm 0.3
## RFE.X.bayesglm 0.5
## RFE.X.glmnet 0.5
## RFE.X.rpart 0.3
## RFE.X.gbm 0.5
## RFE.X.rf 0.3
## RFE.X.nnet 0.4
## RFE.X.avNNet 0.4
## RFE.X.earth 0.6
## All.X.glmnet 0.5
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.5
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.5
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.5
## Max.cor.Y.rcv.3X5.range.glmnet 0.5
## Max.cor.Y.rcv.3X5.pca.glmnet 0.6
## Max.cor.Y.rcv.3X5.ica.glmnet 0.1
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5
## max.f.score.OOB max.Accuracy.OOB
## MFO.myMFO_classfr 0.8601583 0.7546296
## Random.myrandom_classfr 0.8601583 0.7546296
## Max.cor.Y.rcv.1X1.glmnet 0.9151079 0.8634259
## Max.cor.Y.rcv.3X1.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X3.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X5.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.5X1.glmnet 0.9151079 0.8634259
## Max.cor.Y.rcv.5X3.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.5X5.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8701473 0.7754630
## Max.cor.Y.rpart 0.8601583 0.7546296
## Interact.High.cor.Y.glmnet 0.9127907 0.8611111
## Low.cor.X.glmnet 0.8892045 0.8194444
## RFE.X.glm 0.8744589 0.7986111
## RFE.X.bayesglm 0.8941176 0.8333333
## RFE.X.glmnet 0.8901569 0.8217593
## RFE.X.rpart 0.8601583 0.7546296
## RFE.X.gbm 0.8798842 0.8078704
## RFE.X.rf 0.8901252 0.8171296
## RFE.X.nnet 0.8959538 0.8333333
## RFE.X.avNNet 0.8978417 0.8356481
## RFE.X.earth 0.8935532 0.8356481
## All.X.glmnet 0.8901569 0.8217593
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.9151079 0.8634259
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X5.range.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8842730 0.8194444
## Max.cor.Y.rcv.3X5.ica.glmnet 0.8601583 0.7546296
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.9166667 0.8657407
## max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.7112313
## Random.myrandom_classfr 0.7112313
## Max.cor.Y.rcv.1X1.glmnet 0.8273915
## Max.cor.Y.rcv.3X1.glmnet 0.8299202
## Max.cor.Y.rcv.3X3.glmnet 0.8299202
## Max.cor.Y.rcv.3X5.glmnet 0.8299202
## Max.cor.Y.rcv.5X1.glmnet 0.8273915
## Max.cor.Y.rcv.5X3.glmnet 0.8299202
## Max.cor.Y.rcv.5X5.glmnet 0.8299202
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7331391
## Max.cor.Y.rpart 0.7112313
## Interact.High.cor.Y.glmnet 0.8248659
## Low.cor.X.glmnet 0.7798653
## RFE.X.glm 0.7576451
## RFE.X.bayesglm 0.7947758
## RFE.X.glmnet 0.7823447
## RFE.X.rpart 0.7112313
## RFE.X.gbm 0.7675003
## RFE.X.rf 0.7773880
## RFE.X.nnet 0.7947758
## RFE.X.avNNet 0.7972691
## RFE.X.earth 0.7972691
## All.X.glmnet 0.7823447
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.8299202
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8273915
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8299202
## Max.cor.Y.rcv.3X5.range.glmnet 0.8299202
## Max.cor.Y.rcv.3X5.pca.glmnet 0.7798653
## Max.cor.Y.rcv.3X5.ica.glmnet 0.7112313
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8299202
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.7945009 0.0000000
## Random.myrandom_classfr 0.7945009 0.0000000
## Max.cor.Y.rcv.1X1.glmnet 0.8943819 0.5727218
## Max.cor.Y.rcv.3X1.glmnet 0.8964488 0.5784089
## Max.cor.Y.rcv.3X3.glmnet 0.8964488 0.5784089
## Max.cor.Y.rcv.3X5.glmnet 0.8964488 0.5784089
## Max.cor.Y.rcv.5X1.glmnet 0.8943819 0.5727218
## Max.cor.Y.rcv.5X3.glmnet 0.8964488 0.5784089
## Max.cor.Y.rcv.5X5.glmnet 0.8964488 0.5784089
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8139670 0.1308388
## Max.cor.Y.rpart 0.7945009 0.0000000
## Interact.High.cor.Y.glmnet 0.8923117 0.5764152
## Low.cor.X.glmnet 0.8545804 0.4157303
## RFE.X.glm 0.8354303 0.3745590
## RFE.X.bayesglm 0.8672487 0.5059093
## RFE.X.glmnet 0.8566975 0.4297470
## RFE.X.rpart 0.7945009 0.0000000
## RFE.X.gbm 0.8439622 0.4076522
## RFE.X.rf 0.8524611 0.3723240
## RFE.X.nnet 0.8672487 0.4842817
## RFE.X.avNNet 0.8693518 0.4858177
## RFE.X.earth 0.8693518 0.5339452
## All.X.glmnet 0.8566975 0.4297470
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.8964488 0.5784089
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8943819 0.5727218
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8964488 0.5784089
## Max.cor.Y.rcv.3X5.range.glmnet 0.8964488 0.5784089
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8545804 0.4757282
## Max.cor.Y.rcv.3X5.ica.glmnet 0.7945009 0.0000000
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8964488 0.5784089
## max.AccuracySD.fit max.KappaSD.fit
## MFO.myMFO_classfr NA NA
## Random.myrandom_classfr NA NA
## Max.cor.Y.rcv.1X1.glmnet NA NA
## Max.cor.Y.rcv.3X1.glmnet 0.01076837 0.04682919
## Max.cor.Y.rcv.3X3.glmnet 0.02087114 0.05769317
## Max.cor.Y.rcv.3X5.glmnet 0.02507525 0.07638378
## Max.cor.Y.rcv.5X1.glmnet 0.04135840 0.13487601
## Max.cor.Y.rcv.5X3.glmnet 0.03207616 0.11084850
## Max.cor.Y.rcv.5X5.glmnet 0.03026295 0.10473857
## Max.cor.Y.rcv.1X1.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.02861030 0.10130948
## Interact.High.cor.Y.glmnet 0.02217580 0.06195974
## Low.cor.X.glmnet 0.01728829 0.04800516
## RFE.X.glm 0.03034377 0.06479159
## RFE.X.bayesglm 0.02818922 0.05806897
## RFE.X.glmnet 0.01797050 0.05239318
## RFE.X.rpart 0.02210169 0.08736425
## RFE.X.gbm 0.01749072 0.05864702
## RFE.X.rf NA NA
## RFE.X.nnet 0.02496325 0.04941710
## RFE.X.avNNet 0.01716327 0.02674727
## RFE.X.earth 0.02390431 0.07977886
## All.X.glmnet 0.01797050 0.05239318
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.02087114 0.05769317
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.02152790 0.05758805
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.02087114 0.05769317
## Max.cor.Y.rcv.3X5.range.glmnet 0.02087114 0.05769317
## Max.cor.Y.rcv.3X5.pca.glmnet 0.02008542 0.06116888
## Max.cor.Y.rcv.3X5.ica.glmnet 0.01509475 0.03918023
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.01663876 0.05469216rm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE, label.minor="teardown")## label step_major step_minor
## 15 fit.models_1_Max.cor.Y.rcv.3X5.Interact 4 1
## 16 fit.models_1_end 5 0
## label_minor bgn end elapsed
## 15 glmnet 418.205 459.606 41.401
## 16 teardown 459.607 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 11 fit.models 6 1 1 260.166 459.616 199.45
## 12 fit.models 6 2 2 459.616 NA NA#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
# if (!is.null(glbMdlMetricSummaryFn)) {
# stats_df <- glb_models_df[, "id", FALSE]
#
# stats_mdl_df <- data.frame()
# for (mdl_id in stats_df$id) {
# stats_mdl_df <- rbind(stats_mdl_df,
# mypredict_mdl(glb_models_lst[[mdl_id]], glb_fitobs_df, glb_rsp_var,
# glb_rsp_var_out, mdl_id, "fit",
# glbMdlMetricSummaryFn, glbMdlMetricSummary,
# glbMdlMetricMaximize, ret_type="stats"))
# }
# stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
#
# stats_mdl_df <- data.frame()
# for (mdl_id in stats_df$id) {
# stats_mdl_df <- rbind(stats_mdl_df,
# mypredict_mdl(glb_models_lst[[mdl_id]], glb_OOBobs_df, glb_rsp_var,
# glb_rsp_var_out, mdl_id, "OOB",
# glbMdlMetricSummaryFn, glbMdlMetricSummary,
# glbMdlMetricMaximize, ret_type="stats"))
# }
# stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
#
# print("Merging following data into glb_models_df:")
# print(stats_mrg_df <- stats_df[, c(1, grep(glbMdlMetricSummary, names(stats_df)))])
# print(tmp_models_df <- orderBy(~id, glb_models_df[, c("id",
# grep(glbMdlMetricSummary, names(stats_df), value=TRUE))]))
#
# tmp2_models_df <- glb_models_df[, c("id", setdiff(names(glb_models_df),
# grep(glbMdlMetricSummary, names(stats_df), value=TRUE)))]
# tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
# print(tmp3_models_df)
# print(names(tmp3_models_df))
# print(glb_models_df <- subset(tmp3_models_df, select=-id.1))
# }
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet Low.cor.X.glmnet
## RFE.X.glm RFE.X.glm
## RFE.X.bayesglm RFE.X.bayesglm
## RFE.X.glmnet RFE.X.glmnet
## RFE.X.rpart RFE.X.rpart
## RFE.X.gbm RFE.X.gbm
## RFE.X.rf RFE.X.rf
## RFE.X.nnet RFE.X.nnet
## RFE.X.avNNet RFE.X.avNNet
## RFE.X.earth RFE.X.earth
## All.X.glmnet All.X.glmnet
## Max.cor.Y.rcv.3X5.Interact.glmnet Max.cor.Y.rcv.3X5.Interact.glmnet
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet Max.cor.Y.rcv.3X5.YeoJohnson.glmnet
## Max.cor.Y.rcv.3X5.center.scale.glmnet Max.cor.Y.rcv.3X5.center.scale.glmnet
## Max.cor.Y.rcv.3X5.range.glmnet Max.cor.Y.rcv.3X5.range.glmnet
## Max.cor.Y.rcv.3X5.pca.glmnet Max.cor.Y.rcv.3X5.pca.glmnet
## Max.cor.Y.rcv.3X5.ica.glmnet Max.cor.Y.rcv.3X5.ica.glmnet
## Max.cor.Y.rcv.3X5.spatialSign.glmnet Max.cor.Y.rcv.3X5.spatialSign.glmnet
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X1.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X3.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.5X1.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.5X3.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.5X5.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.1X1.cp.0.rpart spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rpart spdiff.cut.fctr,sprice.root2
## Interact.High.cor.Y.glmnet spdiff.cut.fctr,sprice.root2,spdiff.cut.fctr:sprice.log10,spdiff.cut.fctr:startprice.dcm2.is9,spdiff.cut.fctr:cellular.fctr,spdiff.cut.fctr:sprice.root2
## Low.cor.X.glmnet spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.chrs.pnct11.n.log,D.weight.post.stop.sum,D.chrs.pnct13.n.log,D.chrs.n.log,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,.rnorm,prdl.descr.my.fctr,startprice.dgt2.is9,storage.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,cellular.fctr,condition.fctr,startprice.dgt1.is9,sprice.root2
## RFE.X.glm spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## RFE.X.bayesglm spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## RFE.X.glmnet spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,.rnorm,cellular.fctr:carrier.fctr
## RFE.X.rpart spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.gbm spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.rf spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.nnet spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.avNNet spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## RFE.X.earth spdiff.cut.fctr,sprice.d20nexp,sprice.root2,sprice.log10,cellular.fctr,startprice.dgt1.is9,condition.fctr,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,prdl.descr.my.fctr,D.terms.post.stop.n.log,startprice.dcm1.is9,D.ratio.wrds.stop.n.wrds.n,D.weight.post.stop.sum,D.weight.sum,startprice.dcm2.is9,D.weight.post.stem.sum,startprice.dgt2.is9,D.wrds.n.log,D.chrs.pnct11.n.log,D.chrs.n.log,color.fctr,D.wrds.stop.n.log,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,storage.fctr,cellular.fctr:carrier.fctr
## All.X.glmnet spdiff.cut.fctr,sprice.d20nexp,startprice.dcm2.is9,startprice.dcm1.is9,D.wrds.stop.n.log,D.chrs.pnct11.n.log,D.weight.post.stop.sum,D.chrs.pnct13.n.log,D.chrs.n.log,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,.rnorm,prdl.descr.my.fctr,startprice.dgt2.is9,storage.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,cellular.fctr,condition.fctr,startprice.dgt1.is9,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr
## Max.cor.Y.rcv.3X5.Interact.glmnet sprice.root2,spdiff.cut.fctr
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.center.scale.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.range.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.pca.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.ica.glmnet spdiff.cut.fctr,sprice.root2
## Max.cor.Y.rcv.3X5.spatialSign.glmnet spdiff.cut.fctr,sprice.root2
## max.nTuningRuns max.AUCpROC.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5054945
## Max.cor.Y.rcv.1X1.glmnet 0 0.7536630
## Max.cor.Y.rcv.3X1.glmnet 25 0.7481685
## Max.cor.Y.rcv.3X3.glmnet 25 0.7481685
## Max.cor.Y.rcv.3X5.glmnet 25 0.7481685
## Max.cor.Y.rcv.5X1.glmnet 25 0.7536630
## Max.cor.Y.rcv.5X3.glmnet 25 0.7481685
## Max.cor.Y.rcv.5X5.glmnet 25 0.7481685
## Max.cor.Y.rcv.1X1.cp.0.rpart 0 0.7685440
## Max.cor.Y.rpart 5 0.8031136
## Interact.High.cor.Y.glmnet 25 0.7639652
## Low.cor.X.glmnet 25 0.7939560
## RFE.X.glm 1 0.8472985
## RFE.X.bayesglm 1 0.8269231
## RFE.X.glmnet 25 0.8104396
## RFE.X.rpart 5 0.8031136
## RFE.X.gbm 25 0.8191392
## RFE.X.rf 5 0.9983974
## RFE.X.nnet 25 0.8450092
## RFE.X.avNNet 25 0.8576007
## RFE.X.earth 5 0.8150183
## All.X.glmnet 25 0.8104396
## Max.cor.Y.rcv.3X5.Interact.glmnet 25 0.7481685
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 25 0.7536630
## Max.cor.Y.rcv.3X5.center.scale.glmnet 25 0.7481685
## Max.cor.Y.rcv.3X5.range.glmnet 25 0.7481685
## Max.cor.Y.rcv.3X5.pca.glmnet 25 0.7213828
## Max.cor.Y.rcv.3X5.ica.glmnet 25 0.5975275
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 25 0.7451923
## max.Sens.fit max.Spec.fit
## MFO.myMFO_classfr 0.0000000 1.0000000
## Random.myrandom_classfr 0.2417582 0.7692308
## Max.cor.Y.rcv.1X1.glmnet 0.5714286 0.9358974
## Max.cor.Y.rcv.3X1.glmnet 0.5604396 0.9358974
## Max.cor.Y.rcv.3X3.glmnet 0.5604396 0.9358974
## Max.cor.Y.rcv.3X5.glmnet 0.5604396 0.9358974
## Max.cor.Y.rcv.5X1.glmnet 0.5714286 0.9358974
## Max.cor.Y.rcv.5X3.glmnet 0.5604396 0.9358974
## Max.cor.Y.rcv.5X5.glmnet 0.5604396 0.9358974
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.6043956 0.9326923
## Max.cor.Y.rpart 0.7472527 0.8589744
## Interact.High.cor.Y.glmnet 0.5824176 0.9455128
## Low.cor.X.glmnet 0.6263736 0.9615385
## RFE.X.glm 0.7362637 0.9583333
## RFE.X.bayesglm 0.6923077 0.9615385
## RFE.X.glmnet 0.6593407 0.9615385
## RFE.X.rpart 0.7472527 0.8589744
## RFE.X.gbm 0.6703297 0.9679487
## RFE.X.rf 1.0000000 0.9967949
## RFE.X.nnet 0.7252747 0.9647436
## RFE.X.avNNet 0.7472527 0.9679487
## RFE.X.earth 0.6813187 0.9487179
## All.X.glmnet 0.6593407 0.9615385
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.5604396 0.9358974
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.5714286 0.9358974
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.5604396 0.9358974
## Max.cor.Y.rcv.3X5.range.glmnet 0.5604396 0.9358974
## Max.cor.Y.rcv.3X5.pca.glmnet 0.5164835 0.9262821
## Max.cor.Y.rcv.3X5.ica.glmnet 0.2527473 0.9423077
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5384615 0.9519231
## max.AUCROCR.fit
## MFO.myMFO_classfr 0.5000000
## Random.myrandom_classfr 0.5112179
## Max.cor.Y.rcv.1X1.glmnet 0.8966434
## Max.cor.Y.rcv.3X1.glmnet 0.8976648
## Max.cor.Y.rcv.3X3.glmnet 0.8976648
## Max.cor.Y.rcv.3X5.glmnet 0.8979466
## Max.cor.Y.rcv.5X1.glmnet 0.8966434
## Max.cor.Y.rcv.5X3.glmnet 0.8976648
## Max.cor.Y.rcv.5X5.glmnet 0.8979114
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8748239
## Max.cor.Y.rpart 0.8036419
## Interact.High.cor.Y.glmnet 0.8969956
## Low.cor.X.glmnet 0.9310721
## RFE.X.glm 0.9598126
## RFE.X.bayesglm 0.9455128
## RFE.X.glmnet 0.9328684
## RFE.X.rpart 0.8036419
## RFE.X.gbm 0.9553571
## RFE.X.rf 0.9999824
## RFE.X.nnet 0.9508841
## RFE.X.avNNet 0.9381692
## RFE.X.earth 0.9361968
## All.X.glmnet 0.9328684
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.8976648
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8967843
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8976648
## Max.cor.Y.rcv.3X5.range.glmnet 0.8976648
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8813222
## Max.cor.Y.rcv.3X5.ica.glmnet 0.8153881
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8920999
## opt.prob.threshold.fit
## MFO.myMFO_classfr 0.2
## Random.myrandom_classfr 0.2
## Max.cor.Y.rcv.1X1.glmnet 0.4
## Max.cor.Y.rcv.3X1.glmnet 0.4
## Max.cor.Y.rcv.3X3.glmnet 0.4
## Max.cor.Y.rcv.3X5.glmnet 0.4
## Max.cor.Y.rcv.5X1.glmnet 0.4
## Max.cor.Y.rcv.5X3.glmnet 0.4
## Max.cor.Y.rcv.5X5.glmnet 0.4
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.5
## Max.cor.Y.rpart 0.9
## Interact.High.cor.Y.glmnet 0.5
## Low.cor.X.glmnet 0.5
## RFE.X.glm 0.5
## RFE.X.bayesglm 0.5
## RFE.X.glmnet 0.5
## RFE.X.rpart 0.9
## RFE.X.gbm 0.5
## RFE.X.rf 0.6
## RFE.X.nnet 0.6
## RFE.X.avNNet 0.6
## RFE.X.earth 0.4
## All.X.glmnet 0.5
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.4
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.4
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.4
## Max.cor.Y.rcv.3X5.range.glmnet 0.4
## Max.cor.Y.rcv.3X5.pca.glmnet 0.5
## Max.cor.Y.rcv.3X5.ica.glmnet 0.1
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5
## max.f.score.fit max.Accuracy.fit
## MFO.myMFO_classfr 0.8727273 0.7741935
## Random.myrandom_classfr 0.8727273 0.7741935
## Max.cor.Y.rcv.1X1.glmnet 0.9082569 0.8511166
## Max.cor.Y.rcv.3X1.glmnet 0.9107692 0.8561083
## Max.cor.Y.rcv.3X3.glmnet 0.9107692 0.8528223
## Max.cor.Y.rcv.3X5.glmnet 0.9121726 0.8531306
## Max.cor.Y.rcv.5X1.glmnet 0.9082569 0.8510426
## Max.cor.Y.rcv.5X3.glmnet 0.9107692 0.8536089
## Max.cor.Y.rcv.5X5.glmnet 0.9121726 0.8541198
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.9107981 0.8585608
## Max.cor.Y.rpart 0.8888889 0.8156317
## Interact.High.cor.Y.glmnet 0.9147287 0.8544868
## Low.cor.X.glmnet 0.9287926 0.8412075
## RFE.X.glm 0.9417323 0.7824765
## RFE.X.bayesglm 0.9375000 0.8337694
## RFE.X.glmnet 0.9331260 0.8445059
## RFE.X.rpart 0.8888889 0.8147595
## RFE.X.gbm 0.9378882 0.8221792
## RFE.X.rf 0.9983949 0.8188586
## RFE.X.nnet 0.9487179 0.8221854
## RFE.X.avNNet 0.9519231 0.8370616
## RFE.X.earth 0.9304482 0.8204901
## All.X.glmnet 0.9331260 0.8445059
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.9107692 0.8528223
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.9082569 0.8528223
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.9107692 0.8528223
## Max.cor.Y.rcv.3X5.range.glmnet 0.9107692 0.8528223
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8961240 0.8263313
## Max.cor.Y.rcv.3X5.ica.glmnet 0.8727273 0.7791720
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.9124424 0.8586021
## max.Kappa.fit max.AUCpROC.OOB
## MFO.myMFO_classfr 0.0000000 0.5000000
## Random.myrandom_classfr 0.0000000 0.5212988
## Max.cor.Y.rcv.1X1.glmnet 0.5178849 0.7471640
## Max.cor.Y.rcv.3X1.glmnet 0.5565229 0.7486978
## Max.cor.Y.rcv.3X3.glmnet 0.5488242 0.7486978
## Max.cor.Y.rcv.3X5.glmnet 0.5466137 0.7486978
## Max.cor.Y.rcv.5X1.glmnet 0.5425612 0.7471640
## Max.cor.Y.rcv.5X3.glmnet 0.5444976 0.7486978
## Max.cor.Y.rcv.5X5.glmnet 0.5454265 0.7486978
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.5703866 0.6697824
## Max.cor.Y.rpart 0.4861128 0.7600995
## Interact.High.cor.Y.glmnet 0.5516326 0.7205116
## Low.cor.X.glmnet 0.4975288 0.6734576
## RFE.X.glm 0.3926215 0.7009202
## RFE.X.bayesglm 0.5153729 0.7304086
## RFE.X.glmnet 0.5106639 0.6813578
## RFE.X.rpart 0.4431850 0.7600995
## RFE.X.gbm 0.4498412 0.6785218
## RFE.X.rf 0.4631079 0.7465563
## RFE.X.nnet 0.4849018 0.7367751
## RFE.X.avNNet 0.5199950 0.7163734
## RFE.X.earth 0.4665753 0.6958560
## All.X.glmnet 0.5106639 0.6813578
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.5488242 0.7486978
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.5516038 0.7471640
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.5488242 0.7486978
## Max.cor.Y.rcv.3X5.range.glmnet 0.5488242 0.7486978
## Max.cor.Y.rcv.3X5.pca.glmnet 0.4556733 0.6610719
## Max.cor.Y.rcv.3X5.ica.glmnet 0.2083131 0.5229772
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5466577 0.7486978
## max.Sens.OOB max.Spec.OOB
## MFO.myMFO_classfr 0.0000000 1.0000000
## Random.myrandom_classfr 0.2358491 0.8067485
## Max.cor.Y.rcv.1X1.glmnet 0.5188679 0.9754601
## Max.cor.Y.rcv.3X1.glmnet 0.5188679 0.9785276
## Max.cor.Y.rcv.3X3.glmnet 0.5188679 0.9785276
## Max.cor.Y.rcv.3X5.glmnet 0.5188679 0.9785276
## Max.cor.Y.rcv.5X1.glmnet 0.5188679 0.9754601
## Max.cor.Y.rcv.5X3.glmnet 0.5188679 0.9785276
## Max.cor.Y.rcv.5X5.glmnet 0.5188679 0.9785276
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.4622642 0.8773006
## Max.cor.Y.rpart 0.6981132 0.8220859
## Interact.High.cor.Y.glmnet 0.4716981 0.9693252
## Low.cor.X.glmnet 0.3867925 0.9601227
## RFE.X.glm 0.5000000 0.9018405
## RFE.X.bayesglm 0.5283019 0.9325153
## RFE.X.glmnet 0.4056604 0.9570552
## RFE.X.rpart 0.6981132 0.8220859
## RFE.X.gbm 0.4245283 0.9325153
## RFE.X.rf 0.5943396 0.8987730
## RFE.X.nnet 0.5471698 0.9263804
## RFE.X.avNNet 0.5094340 0.9233129
## RFE.X.earth 0.4622642 0.9294479
## All.X.glmnet 0.4056604 0.9570552
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.5188679 0.9785276
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.5188679 0.9754601
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.5188679 0.9785276
## Max.cor.Y.rcv.3X5.range.glmnet 0.5188679 0.9785276
## Max.cor.Y.rcv.3X5.pca.glmnet 0.3773585 0.9447853
## Max.cor.Y.rcv.3X5.ica.glmnet 0.1226415 0.9233129
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5188679 0.9785276
## max.AUCROCR.OOB
## MFO.myMFO_classfr 0.5000000
## Random.myrandom_classfr 0.5310800
## Max.cor.Y.rcv.1X1.glmnet 0.8730032
## Max.cor.Y.rcv.3X1.glmnet 0.8747106
## Max.cor.Y.rcv.3X3.glmnet 0.8747106
## Max.cor.Y.rcv.3X5.glmnet 0.8749132
## Max.cor.Y.rcv.5X1.glmnet 0.8730032
## Max.cor.Y.rcv.5X3.glmnet 0.8747106
## Max.cor.Y.rcv.5X5.glmnet 0.8747974
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8205087
## Max.cor.Y.rpart 0.7542540
## Interact.High.cor.Y.glmnet 0.8736399
## Low.cor.X.glmnet 0.8467126
## RFE.X.glm 0.8206390
## RFE.X.bayesglm 0.8568411
## RFE.X.glmnet 0.8419956
## RFE.X.rpart 0.7542540
## RFE.X.gbm 0.8382625
## RFE.X.rf 0.8557414
## RFE.X.nnet 0.8596336
## RFE.X.avNNet 0.8506048
## RFE.X.earth 0.8513283
## All.X.glmnet 0.8419956
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.8747106
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8712669
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8747106
## Max.cor.Y.rcv.3X5.range.glmnet 0.8747106
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8420679
## Max.cor.Y.rcv.3X5.ica.glmnet 0.7595642
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8697911
## opt.prob.threshold.OOB
## MFO.myMFO_classfr 0.2
## Random.myrandom_classfr 0.2
## Max.cor.Y.rcv.1X1.glmnet 0.5
## Max.cor.Y.rcv.3X1.glmnet 0.5
## Max.cor.Y.rcv.3X3.glmnet 0.5
## Max.cor.Y.rcv.3X5.glmnet 0.5
## Max.cor.Y.rcv.5X1.glmnet 0.5
## Max.cor.Y.rcv.5X3.glmnet 0.5
## Max.cor.Y.rcv.5X5.glmnet 0.5
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.2
## Max.cor.Y.rpart 0.3
## Interact.High.cor.Y.glmnet 0.6
## Low.cor.X.glmnet 0.5
## RFE.X.glm 0.3
## RFE.X.bayesglm 0.5
## RFE.X.glmnet 0.5
## RFE.X.rpart 0.3
## RFE.X.gbm 0.5
## RFE.X.rf 0.3
## RFE.X.nnet 0.4
## RFE.X.avNNet 0.4
## RFE.X.earth 0.6
## All.X.glmnet 0.5
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.5
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.5
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.5
## Max.cor.Y.rcv.3X5.range.glmnet 0.5
## Max.cor.Y.rcv.3X5.pca.glmnet 0.6
## Max.cor.Y.rcv.3X5.ica.glmnet 0.1
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5
## max.f.score.OOB max.Accuracy.OOB
## MFO.myMFO_classfr 0.8601583 0.7546296
## Random.myrandom_classfr 0.8601583 0.7546296
## Max.cor.Y.rcv.1X1.glmnet 0.9151079 0.8634259
## Max.cor.Y.rcv.3X1.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X3.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X5.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.5X1.glmnet 0.9151079 0.8634259
## Max.cor.Y.rcv.5X3.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.5X5.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8701473 0.7754630
## Max.cor.Y.rpart 0.8601583 0.7546296
## Interact.High.cor.Y.glmnet 0.9127907 0.8611111
## Low.cor.X.glmnet 0.8892045 0.8194444
## RFE.X.glm 0.8744589 0.7986111
## RFE.X.bayesglm 0.8941176 0.8333333
## RFE.X.glmnet 0.8901569 0.8217593
## RFE.X.rpart 0.8601583 0.7546296
## RFE.X.gbm 0.8798842 0.8078704
## RFE.X.rf 0.8901252 0.8171296
## RFE.X.nnet 0.8959538 0.8333333
## RFE.X.avNNet 0.8978417 0.8356481
## RFE.X.earth 0.8935532 0.8356481
## All.X.glmnet 0.8901569 0.8217593
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.9151079 0.8634259
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X5.range.glmnet 0.9166667 0.8657407
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8842730 0.8194444
## Max.cor.Y.rcv.3X5.ica.glmnet 0.8601583 0.7546296
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.9166667 0.8657407
## max.Kappa.OOB
## MFO.myMFO_classfr 0.0000000
## Random.myrandom_classfr 0.0000000
## Max.cor.Y.rcv.1X1.glmnet 0.5727218
## Max.cor.Y.rcv.3X1.glmnet 0.5784089
## Max.cor.Y.rcv.3X3.glmnet 0.5784089
## Max.cor.Y.rcv.3X5.glmnet 0.5784089
## Max.cor.Y.rcv.5X1.glmnet 0.5727218
## Max.cor.Y.rcv.5X3.glmnet 0.5784089
## Max.cor.Y.rcv.5X5.glmnet 0.5784089
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.1308388
## Max.cor.Y.rpart 0.0000000
## Interact.High.cor.Y.glmnet 0.5764152
## Low.cor.X.glmnet 0.4157303
## RFE.X.glm 0.3745590
## RFE.X.bayesglm 0.5059093
## RFE.X.glmnet 0.4297470
## RFE.X.rpart 0.0000000
## RFE.X.gbm 0.4076522
## RFE.X.rf 0.3723240
## RFE.X.nnet 0.4842817
## RFE.X.avNNet 0.4858177
## RFE.X.earth 0.5339452
## All.X.glmnet 0.4297470
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.5784089
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.5727218
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.5784089
## Max.cor.Y.rcv.3X5.range.glmnet 0.5784089
## Max.cor.Y.rcv.3X5.pca.glmnet 0.4757282
## Max.cor.Y.rcv.3X5.ica.glmnet 0.0000000
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5784089
## inv.elapsedtime.everything
## MFO.myMFO_classfr 3.41296928
## Random.myrandom_classfr 3.64963504
## Max.cor.Y.rcv.1X1.glmnet 1.31233596
## Max.cor.Y.rcv.3X1.glmnet 0.71022727
## Max.cor.Y.rcv.3X3.glmnet 0.49480455
## Max.cor.Y.rcv.3X5.glmnet 0.39077765
## Max.cor.Y.rcv.5X1.glmnet 0.61050061
## Max.cor.Y.rcv.5X3.glmnet 0.41736227
## Max.cor.Y.rcv.5X5.glmnet 0.26983270
## Max.cor.Y.rcv.1X1.cp.0.rpart 1.36798906
## Max.cor.Y.rpart 0.66533599
## Interact.High.cor.Y.glmnet 0.21344717
## Low.cor.X.glmnet 0.09704969
## RFE.X.glm 0.51948052
## RFE.X.bayesglm 0.31318509
## RFE.X.glmnet 0.08975049
## RFE.X.rpart 0.40617384
## RFE.X.gbm 0.19623234
## RFE.X.rf 0.25706941
## RFE.X.nnet 0.05654829
## RFE.X.avNNet 0.02173441
## RFE.X.earth 0.18142235
## All.X.glmnet 0.08942144
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.46641791
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.40849673
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.44189129
## Max.cor.Y.rcv.3X5.range.glmnet 0.44503783
## Max.cor.Y.rcv.3X5.pca.glmnet 0.46082949
## Max.cor.Y.rcv.3X5.ica.glmnet 0.40733198
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.43365134
## inv.elapsedtime.final
## MFO.myMFO_classfr 333.3333333
## Random.myrandom_classfr 1000.0000000
## Max.cor.Y.rcv.1X1.glmnet 33.3333333
## Max.cor.Y.rcv.3X1.glmnet 52.6315789
## Max.cor.Y.rcv.3X3.glmnet 50.0000000
## Max.cor.Y.rcv.3X5.glmnet 47.6190476
## Max.cor.Y.rcv.5X1.glmnet 52.6315789
## Max.cor.Y.rcv.5X3.glmnet 52.6315789
## Max.cor.Y.rcv.5X5.glmnet 45.4545455
## Max.cor.Y.rcv.1X1.cp.0.rpart 76.9230769
## Max.cor.Y.rpart 83.3333333
## Interact.High.cor.Y.glmnet 2.2779043
## Low.cor.X.glmnet 2.1645022
## RFE.X.glm 11.7647059
## RFE.X.bayesglm 5.9171598
## RFE.X.glmnet 1.4025245
## RFE.X.rpart 25.6410256
## RFE.X.gbm 4.8543689
## RFE.X.rf 0.7057163
## RFE.X.nnet 14.4927536
## RFE.X.avNNet 5.9523810
## RFE.X.earth 1.5408320
## All.X.glmnet 1.4084507
## Max.cor.Y.rcv.3X5.Interact.glmnet 50.0000000
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 25.0000000
## Max.cor.Y.rcv.3X5.center.scale.glmnet 40.0000000
## Max.cor.Y.rcv.3X5.range.glmnet 41.6666667
## Max.cor.Y.rcv.3X5.pca.glmnet 47.6190476
## Max.cor.Y.rcv.3X5.ica.glmnet 47.6190476
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 33.3333333print(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 30. Consider specifying shapes manually if you must have them.## Warning: Removed 480 rows containing missing values (geom_point).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 30. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 7 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 7 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id max.AUCROCR.OOB max.AUCpROC.OOB
## 6 Max.cor.Y.rcv.3X5.glmnet 0.8749132 0.7486978
## 9 Max.cor.Y.rcv.5X5.glmnet 0.8747974 0.7486978
## 4 Max.cor.Y.rcv.3X1.glmnet 0.8747106 0.7486978
## 8 Max.cor.Y.rcv.5X3.glmnet 0.8747106 0.7486978
## 5 Max.cor.Y.rcv.3X3.glmnet 0.8747106 0.7486978
## 24 Max.cor.Y.rcv.3X5.Interact.glmnet 0.8747106 0.7486978
## 26 Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8747106 0.7486978
## 27 Max.cor.Y.rcv.3X5.range.glmnet 0.8747106 0.7486978
## 12 Interact.High.cor.Y.glmnet 0.8736399 0.7205116
## 3 Max.cor.Y.rcv.1X1.glmnet 0.8730032 0.7471640
## 7 Max.cor.Y.rcv.5X1.glmnet 0.8730032 0.7471640
## 25 Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8712669 0.7471640
## 30 Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8697911 0.7486978
## 20 RFE.X.nnet 0.8596336 0.7367751
## 15 RFE.X.bayesglm 0.8568411 0.7304086
## 19 RFE.X.rf 0.8557414 0.7465563
## 22 RFE.X.earth 0.8513283 0.6958560
## 21 RFE.X.avNNet 0.8506048 0.7163734
## 13 Low.cor.X.glmnet 0.8467126 0.6734576
## 28 Max.cor.Y.rcv.3X5.pca.glmnet 0.8420679 0.6610719
## 16 RFE.X.glmnet 0.8419956 0.6813578
## 23 All.X.glmnet 0.8419956 0.6813578
## 18 RFE.X.gbm 0.8382625 0.6785218
## 14 RFE.X.glm 0.8206390 0.7009202
## 10 Max.cor.Y.rcv.1X1.cp.0.rpart 0.8205087 0.6697824
## 29 Max.cor.Y.rcv.3X5.ica.glmnet 0.7595642 0.5229772
## 11 Max.cor.Y.rpart 0.7542540 0.7600995
## 17 RFE.X.rpart 0.7542540 0.7600995
## 2 Random.myrandom_classfr 0.5310800 0.5212988
## 1 MFO.myMFO_classfr 0.5000000 0.5000000
## max.Accuracy.OOB max.Accuracy.fit opt.prob.threshold.fit
## 6 0.8657407 0.8531306 0.4
## 9 0.8657407 0.8541198 0.4
## 4 0.8657407 0.8561083 0.4
## 8 0.8657407 0.8536089 0.4
## 5 0.8657407 0.8528223 0.4
## 24 0.8657407 0.8528223 0.4
## 26 0.8657407 0.8528223 0.4
## 27 0.8657407 0.8528223 0.4
## 12 0.8611111 0.8544868 0.5
## 3 0.8634259 0.8511166 0.4
## 7 0.8634259 0.8510426 0.4
## 25 0.8634259 0.8528223 0.4
## 30 0.8657407 0.8586021 0.5
## 20 0.8333333 0.8221854 0.6
## 15 0.8333333 0.8337694 0.5
## 19 0.8171296 0.8188586 0.6
## 22 0.8356481 0.8204901 0.4
## 21 0.8356481 0.8370616 0.6
## 13 0.8194444 0.8412075 0.5
## 28 0.8194444 0.8263313 0.5
## 16 0.8217593 0.8445059 0.5
## 23 0.8217593 0.8445059 0.5
## 18 0.8078704 0.8221792 0.5
## 14 0.7986111 0.7824765 0.5
## 10 0.7754630 0.8585608 0.5
## 29 0.7546296 0.7791720 0.1
## 11 0.7546296 0.8156317 0.9
## 17 0.7546296 0.8147595 0.9
## 2 0.7546296 0.7741935 0.2
## 1 0.7546296 0.7741935 0.2
## opt.prob.threshold.OOB
## 6 0.5
## 9 0.5
## 4 0.5
## 8 0.5
## 5 0.5
## 24 0.5
## 26 0.5
## 27 0.5
## 12 0.6
## 3 0.5
## 7 0.5
## 25 0.5
## 30 0.5
## 20 0.4
## 15 0.5
## 19 0.3
## 22 0.6
## 21 0.4
## 13 0.5
## 28 0.6
## 16 0.5
## 23 0.5
## 18 0.5
## 14 0.3
## 10 0.2
## 29 0.1
## 11 0.3
## 17 0.3
## 2 0.2
## 1 0.2print(myplot_radar(radar_inp_df = dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 30. Consider specifying shapes manually if you must have them.## Warning: Removed 168 rows containing missing values (geom_point).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 30. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~-max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7fe559e91d78>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: Max.cor.Y.rcv.3X5.glmnet"glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
#rsp_var_out <- paste0(rsp_var_out, mdl_id)
rsp_var_out <- paste0(glb_rsp_var, ".predict.")
predct_var_name <- paste0(rsp_var_out, mdl_id)
predct_prob_var_name <- paste0(rsp_var_out, mdl_id, ".prob")
predct_accurate_var_name <- paste0(rsp_var_out, mdl_id, ".accurate")
predct_error_var_name <- paste0(rsp_var_out, mdl_id, ".err")
predct_erabs_var_name <- paste0(rsp_var_out, mdl_id, ".err.abs")
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glb_category_var), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glb_category_var), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glb_category_var), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
df[, paste0(predct_var_name, ".prob")] <-
predict(mdl, newdata = df, type = "prob")
stop("Multinomial prediction error calculation needs to be implemented...")
}
return(df)
}
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df; glb_trnobs_df <- sav_trnobs_df; glb_fitobs_df <- sav_fitobs_df; glb_OOBobs_df <- sav_OOBobs_df; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- paste0(glb_rsp_var_out, mdl_id)
predct_error_var_name <- paste0(glb_rsp_var_out, mdl_id, ".err.abs")
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var_out)
tmp_obs_df <- obs_df %>%
dplyr::select_(glb_category_var, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glb_category_var) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glb_category_var,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glb_category_var, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
if (glb_mdl_ensemble == "auto") {
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
# mdl_threshold_pos <- min(which(tmp_models_df$id %in%
# c("MFO.myMFO_classfr", "Baseline.mybaseln_classfr"))) - 1
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
glb_mdl_ensemble <- tmp_models_df$id[1:mdl_threshold_pos]
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glb_fitobs_df <- glb_get_predictions(df = glb_fitobs_df, mdl_id,
glb_rsp_var_out)
glb_OOBobs_df <- glb_get_predictions(df = glb_OOBobs_df, mdl_id,
glb_rsp_var_out)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(glb_rsp_var_out, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glb_fitobs_df[, glb_rsp_var], glb_fitobs_df[, paste(glb_rsp_var_out, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, "Ensemble.glmnet", glb_rsp_var_out);print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indep_vars <- paste(glb_rsp_var_out, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indep_vars <- paste(indep_vars, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indep_vars <- intersect(indep_vars, names(glb_fitobs_df))
# indep_vars <- grep(glb_rsp_var_out, names(glb_fitobs_df), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
# if (glb_is_classification && glb_is_binomial)
# indep_vars <- grep("prob$", indep_vars, value=TRUE) else
# indep_vars <- indep_vars[!grepl("err$", indep_vars)]
#rfe_fit_ens_results <- myrun_rfe(glb_fitobs_df, indep_vars)
for (method in c("glmnet")) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, tune.df = NULL,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
}
dsp_models_df <- get_dsp_models_df()
}## Warning in if (glb_mdl_ensemble == "auto") {: the condition has length > 1
## and only the first element will be used## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning: Model RFE.X.Interact.glmnet in glb_model_ensemble not found !## [1] "fitting model: Ensemble.glmnet"
## [1] " indep_vars: sold.fctr.predict.RFE.X.rpart.prob,sold.fctr.predict.RFE.X.earth.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.RFE.X.bayesglm.prob,sold.fctr.predict.RFE.X.nnet.prob,sold.fctr.predict.RFE.X.avNNet.prob,sold.fctr.predict.RFE.X.gbm.prob"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00143 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -4.2582207
## sold.fctr.predict.RFE.X.avNNet.prob
## 2.3995941
## sold.fctr.predict.RFE.X.bayesglm.prob
## -5.7093712
## sold.fctr.predict.RFE.X.earth.prob
## 0.3609861
## sold.fctr.predict.RFE.X.gbm.prob
## 7.8639851
## sold.fctr.predict.RFE.X.glm.prob
## 6.0095411
## sold.fctr.predict.RFE.X.glmnet.prob
## -2.9324729
## sold.fctr.predict.RFE.X.nnet.prob
## 2.5241463
## sold.fctr.predict.RFE.X.rpart.prob
## -2.3063520
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -4.2638307
## sold.fctr.predict.RFE.X.avNNet.prob
## 2.4184872
## sold.fctr.predict.RFE.X.bayesglm.prob
## -6.0646505
## sold.fctr.predict.RFE.X.earth.prob
## 0.3793799
## sold.fctr.predict.RFE.X.gbm.prob
## 7.9833175
## sold.fctr.predict.RFE.X.glm.prob
## 6.1715359
## sold.fctr.predict.RFE.X.glmnet.prob
## -2.9582634
## sold.fctr.predict.RFE.X.nnet.prob
## 2.6304033
## sold.fctr.predict.RFE.X.rpart.prob
## -2.3335854
## sold.fctr sold.fctr.predict.Ensemble.glmnet.N
## 1 N 75
## 2 Y 8
## sold.fctr.predict.Ensemble.glmnet.Y
## 1 16
## 2 304
## Prediction
## Reference N Y
## N 75 16
## Y 8 304
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.404467e-01 8.241966e-01 9.126826e-01 9.614740e-01 7.741935e-01
## AccuracyPValue McnemarPValue
## 7.933112e-20 1.530419e-01
## sold.fctr sold.fctr.predict.Ensemble.glmnet.N
## 1 N 45
## 2 Y 19
## sold.fctr.predict.Ensemble.glmnet.Y
## 1 61
## 2 307
## Prediction
## Reference N Y
## N 45 61
## Y 19 307
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.148148e-01 4.227686e-01 7.749129e-01 8.503396e-01 7.546296e-01
## AccuracyPValue McnemarPValue
## 1.700050e-03 4.562966e-06
## id
## 1 Ensemble.glmnet
## feats
## 1 sold.fctr.predict.RFE.X.rpart.prob,sold.fctr.predict.RFE.X.earth.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.RFE.X.bayesglm.prob,sold.fctr.predict.RFE.X.nnet.prob,sold.fctr.predict.RFE.X.avNNet.prob,sold.fctr.predict.RFE.X.gbm.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.388 0.035
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8992674 0.8241758 0.974359 0.9744999
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9620253 0.9321786
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9126826 0.961474 0.7998243
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7134217 0.5188679 0.9079755 0.8277578
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.8847262 0.8148148
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7749129 0.8503396 0.4227686
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01916062 0.05683443
## [1] "Cross Validation issues:"## Warning in get_dsp_models_df(): Cross Validation issues:## MFO.myMFO_classfr Random.myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.cp.0.rpart
## 0 0
if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id)) ## [1] "User specified selection: Ensemble.glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 100 -none- numeric
## beta 800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -4.2582207
## sold.fctr.predict.RFE.X.avNNet.prob
## 2.3995941
## sold.fctr.predict.RFE.X.bayesglm.prob
## -5.7093712
## sold.fctr.predict.RFE.X.earth.prob
## 0.3609861
## sold.fctr.predict.RFE.X.gbm.prob
## 7.8639851
## sold.fctr.predict.RFE.X.glm.prob
## 6.0095411
## sold.fctr.predict.RFE.X.glmnet.prob
## -2.9324729
## sold.fctr.predict.RFE.X.nnet.prob
## 2.5241463
## sold.fctr.predict.RFE.X.rpart.prob
## -2.3063520
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -4.2638307
## sold.fctr.predict.RFE.X.avNNet.prob
## 2.4184872
## sold.fctr.predict.RFE.X.bayesglm.prob
## -6.0646505
## sold.fctr.predict.RFE.X.earth.prob
## 0.3793799
## sold.fctr.predict.RFE.X.gbm.prob
## 7.9833175
## sold.fctr.predict.RFE.X.glm.prob
## 6.1715359
## sold.fctr.predict.RFE.X.glmnet.prob
## -2.9582634
## sold.fctr.predict.RFE.X.nnet.prob
## 2.6304033
## sold.fctr.predict.RFE.X.rpart.prob
## -2.3335854## [1] TRUE#stop(here"); glb_to_sav()
# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "Ensemble.glmnet fit prediction diagnostics:"glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "Ensemble.glmnet OOB prediction diagnostics:"glb_OOBobs_df <- glb_get_predictions(df = glb_OOBobs_df, mdl_id = glb_sel_mdl_id,
rsp_var_out = glb_rsp_var_out)
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".importance")] <- glb_featsimp_df$importance; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.importance) | (abs(RFE.X.YeoJohnson.glmnet.importance - RFE.X.glmnet.importance) > 0.0001), select=-importance)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
print(glb_featsimp_df)## importance
## sold.fctr.predict.RFE.X.gbm.prob 100.00000
## sold.fctr.predict.RFE.X.glm.prob 86.57999
## sold.fctr.predict.RFE.X.nnet.prob 61.05088
## sold.fctr.predict.RFE.X.avNNet.prob 59.94612
## sold.fctr.predict.RFE.X.earth.prob 45.08651
## sold.fctr.predict.RFE.X.rpart.prob 25.54264
## sold.fctr.predict.RFE.X.glmnet.prob 20.98238
## sold.fctr.predict.RFE.X.bayesglm.prob 0.00000
## Ensemble.glmnet.importance
## sold.fctr.predict.RFE.X.gbm.prob 100.00000
## sold.fctr.predict.RFE.X.glm.prob 86.57999
## sold.fctr.predict.RFE.X.nnet.prob 61.05088
## sold.fctr.predict.RFE.X.avNNet.prob 59.94612
## sold.fctr.predict.RFE.X.earth.prob 45.08651
## sold.fctr.predict.RFE.X.rpart.prob 25.54264
## sold.fctr.predict.RFE.X.glmnet.prob 20.98238
## sold.fctr.predict.RFE.X.bayesglm.prob 0.00000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(importance.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id = glb_sel_mdl_id,
prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) 
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.RFE.X.rpart.prob
## 834 11839 Y 0.9213483
## 3 10007 Y 0.9213483
## sold.fctr.predict.RFE.X.rpart sold.fctr.predict.RFE.X.rpart.err
## 834 Y FALSE
## 3 Y FALSE
## sold.fctr.predict.RFE.X.rpart.err.abs
## 834 0.07865169
## 3 0.07865169
## sold.fctr.predict.RFE.X.rpart.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.RFE.X.earth.prob sold.fctr.predict.RFE.X.earth
## 834 1.0000000 Y
## 3 0.9448296 Y
## sold.fctr.predict.RFE.X.earth.err
## 834 FALSE
## 3 FALSE
## sold.fctr.predict.RFE.X.earth.err.abs
## 834 1.643874e-09
## 3 5.517042e-02
## sold.fctr.predict.RFE.X.earth.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.RFE.X.glmnet.prob sold.fctr.predict.RFE.X.glmnet
## 834 0.8274598 Y
## 3 0.8632080 Y
## sold.fctr.predict.RFE.X.glmnet.err
## 834 FALSE
## 3 FALSE
## sold.fctr.predict.RFE.X.glmnet.err.abs
## 834 0.1725402
## 3 0.1367920
## sold.fctr.predict.RFE.X.glmnet.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.RFE.X.glm.prob sold.fctr.predict.RFE.X.glm
## 834 0.9999999 Y
## 3 0.9338027 Y
## sold.fctr.predict.RFE.X.glm.err sold.fctr.predict.RFE.X.glm.err.abs
## 834 FALSE 9.446641e-08
## 3 FALSE 6.619729e-02
## sold.fctr.predict.RFE.X.glm.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.RFE.X.bayesglm.prob sold.fctr.predict.RFE.X.bayesglm
## 834 0.9648679 Y
## 3 0.8974654 Y
## sold.fctr.predict.RFE.X.bayesglm.err
## 834 FALSE
## 3 FALSE
## sold.fctr.predict.RFE.X.bayesglm.err.abs
## 834 0.03513209
## 3 0.10253456
## sold.fctr.predict.RFE.X.bayesglm.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.RFE.X.nnet.prob sold.fctr.predict.RFE.X.nnet
## 834 0.976697 Y
## 3 0.973018 Y
## sold.fctr.predict.RFE.X.nnet.err sold.fctr.predict.RFE.X.nnet.err.abs
## 834 FALSE 0.02330302
## 3 FALSE 0.02698197
## sold.fctr.predict.RFE.X.nnet.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.RFE.X.avNNet.prob sold.fctr.predict.RFE.X.avNNet
## 834 0.9565373 Y
## 3 0.9481951 Y
## sold.fctr.predict.RFE.X.avNNet.err
## 834 FALSE
## 3 FALSE
## sold.fctr.predict.RFE.X.avNNet.err.abs
## 834 0.04346274
## 3 0.05180491
## sold.fctr.predict.RFE.X.avNNet.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.RFE.X.gbm.prob sold.fctr.predict.RFE.X.gbm
## 834 0.9776453 Y
## 3 0.9350854 Y
## sold.fctr.predict.RFE.X.gbm.err sold.fctr.predict.RFE.X.gbm.err.abs
## 834 FALSE 0.02235471
## 3 FALSE 0.06491456
## sold.fctr.predict.RFE.X.gbm.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.Ensemble.glmnet.prob
## 834 0.9891031
## 3 0.9821605
## sold.fctr.predict.Ensemble.glmnet
## 834 Y
## 3 Y
## sold.fctr.predict.Ensemble.glmnet.err
## 834 FALSE
## 3 FALSE
## sold.fctr.predict.Ensemble.glmnet.err.abs
## 834 0.01089691
## 3 0.01783948
## sold.fctr.predict.Ensemble.glmnet.accurate
## 834 TRUE
## 3 TRUE
## sold.fctr.predict.Ensemble.glmnet.error .label
## 834 0 11839
## 3 0 10007
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.RFE.X.rpart.prob
## 655 11302 Y 0.3928571
## 728 11619 Y 0.3928571
## 555 10961 Y 0.3928571
## 290 10501 Y 0.3928571
## 516 10869 Y 0.3928571
## 589 11056 Y 0.3928571
## sold.fctr.predict.RFE.X.rpart sold.fctr.predict.RFE.X.rpart.err
## 655 Y FALSE
## 728 Y FALSE
## 555 Y FALSE
## 290 Y FALSE
## 516 Y FALSE
## 589 Y FALSE
## sold.fctr.predict.RFE.X.rpart.err.abs
## 655 0.6071429
## 728 0.6071429
## 555 0.6071429
## 290 0.6071429
## 516 0.6071429
## 589 0.6071429
## sold.fctr.predict.RFE.X.rpart.accurate
## 655 TRUE
## 728 TRUE
## 555 TRUE
## 290 TRUE
## 516 TRUE
## 589 TRUE
## sold.fctr.predict.RFE.X.earth.prob sold.fctr.predict.RFE.X.earth
## 655 0.76174755 Y
## 728 0.01551256 N
## 555 0.03741678 N
## 290 0.46625247 N
## 516 0.15285262 N
## 589 0.16945972 N
## sold.fctr.predict.RFE.X.earth.err
## 655 FALSE
## 728 TRUE
## 555 TRUE
## 290 TRUE
## 516 TRUE
## 589 TRUE
## sold.fctr.predict.RFE.X.earth.err.abs
## 655 0.2382524
## 728 0.9844874
## 555 0.9625832
## 290 0.5337475
## 516 0.8471474
## 589 0.8305403
## sold.fctr.predict.RFE.X.earth.accurate
## 655 TRUE
## 728 FALSE
## 555 FALSE
## 290 FALSE
## 516 FALSE
## 589 FALSE
## sold.fctr.predict.RFE.X.glmnet.prob sold.fctr.predict.RFE.X.glmnet
## 655 0.51200249 Y
## 728 0.07919943 N
## 555 0.09828407 N
## 290 0.50056291 Y
## 516 0.33787904 N
## 589 0.24409893 N
## sold.fctr.predict.RFE.X.glmnet.err
## 655 FALSE
## 728 TRUE
## 555 TRUE
## 290 FALSE
## 516 TRUE
## 589 TRUE
## sold.fctr.predict.RFE.X.glmnet.err.abs
## 655 0.4879975
## 728 0.9208006
## 555 0.9017159
## 290 0.4994371
## 516 0.6621210
## 589 0.7559011
## sold.fctr.predict.RFE.X.glmnet.accurate
## 655 TRUE
## 728 FALSE
## 555 FALSE
## 290 TRUE
## 516 FALSE
## 589 FALSE
## sold.fctr.predict.RFE.X.glm.prob sold.fctr.predict.RFE.X.glm
## 655 0.0817487804 N
## 728 0.0004892483 N
## 555 0.0271202761 N
## 290 0.2073294436 N
## 516 0.0018641955 N
## 589 0.0286673428 N
## sold.fctr.predict.RFE.X.glm.err sold.fctr.predict.RFE.X.glm.err.abs
## 655 TRUE 0.9182512
## 728 TRUE 0.9995108
## 555 TRUE 0.9728797
## 290 TRUE 0.7926706
## 516 TRUE 0.9981358
## 589 TRUE 0.9713327
## sold.fctr.predict.RFE.X.glm.accurate
## 655 FALSE
## 728 FALSE
## 555 FALSE
## 290 FALSE
## 516 FALSE
## 589 FALSE
## sold.fctr.predict.RFE.X.bayesglm.prob sold.fctr.predict.RFE.X.bayesglm
## 655 0.46724953 N
## 728 0.00826344 N
## 555 0.02374624 N
## 290 0.38291468 N
## 516 0.04457482 N
## 589 0.07827787 N
## sold.fctr.predict.RFE.X.bayesglm.err
## 655 TRUE
## 728 TRUE
## 555 TRUE
## 290 TRUE
## 516 TRUE
## 589 TRUE
## sold.fctr.predict.RFE.X.bayesglm.err.abs
## 655 0.5327505
## 728 0.9917366
## 555 0.9762538
## 290 0.6170853
## 516 0.9554252
## 589 0.9217221
## sold.fctr.predict.RFE.X.bayesglm.accurate
## 655 FALSE
## 728 FALSE
## 555 FALSE
## 290 FALSE
## 516 FALSE
## 589 FALSE
## sold.fctr.predict.RFE.X.nnet.prob sold.fctr.predict.RFE.X.nnet
## 655 0.29190928 N
## 728 0.07378284 N
## 555 0.08973671 N
## 290 0.36324788 N
## 516 0.11497994 N
## 589 0.11150739 N
## sold.fctr.predict.RFE.X.nnet.err sold.fctr.predict.RFE.X.nnet.err.abs
## 655 TRUE 0.7080907
## 728 TRUE 0.9262172
## 555 TRUE 0.9102633
## 290 TRUE 0.6367521
## 516 TRUE 0.8850201
## 589 TRUE 0.8884926
## sold.fctr.predict.RFE.X.nnet.accurate
## 655 FALSE
## 728 FALSE
## 555 FALSE
## 290 FALSE
## 516 FALSE
## 589 FALSE
## sold.fctr.predict.RFE.X.avNNet.prob sold.fctr.predict.RFE.X.avNNet
## 655 0.39022910 N
## 728 0.09158735 N
## 555 0.09455938 N
## 290 0.30731978 N
## 516 0.21006322 N
## 589 0.11172209 N
## sold.fctr.predict.RFE.X.avNNet.err
## 655 TRUE
## 728 TRUE
## 555 TRUE
## 290 TRUE
## 516 TRUE
## 589 TRUE
## sold.fctr.predict.RFE.X.avNNet.err.abs
## 655 0.6097709
## 728 0.9084127
## 555 0.9054406
## 290 0.6926802
## 516 0.7899368
## 589 0.8882779
## sold.fctr.predict.RFE.X.avNNet.accurate
## 655 FALSE
## 728 FALSE
## 555 FALSE
## 290 FALSE
## 516 FALSE
## 589 FALSE
## sold.fctr.predict.RFE.X.gbm.prob sold.fctr.predict.RFE.X.gbm
## 655 0.2310236 N
## 728 0.1076734 N
## 555 0.1321452 N
## 290 0.3098467 N
## 516 0.3126394 N
## 589 0.3192677 N
## sold.fctr.predict.RFE.X.gbm.err sold.fctr.predict.RFE.X.gbm.err.abs
## 655 TRUE 0.7689764
## 728 TRUE 0.8923266
## 555 TRUE 0.8678548
## 290 TRUE 0.6901533
## 516 TRUE 0.6873606
## 589 TRUE 0.6807323
## sold.fctr.predict.RFE.X.gbm.accurate
## 655 FALSE
## 728 FALSE
## 555 FALSE
## 290 FALSE
## 516 FALSE
## 589 FALSE
## sold.fctr.predict.Ensemble.glmnet.prob
## 655 0.006006819
## 728 0.015038924
## 555 0.019503251
## 290 0.034688145
## 516 0.043710731
## 589 0.046071503
## sold.fctr.predict.Ensemble.glmnet
## 655 N
## 728 N
## 555 N
## 290 N
## 516 N
## 589 N
## sold.fctr.predict.Ensemble.glmnet.err
## 655 TRUE
## 728 TRUE
## 555 TRUE
## 290 TRUE
## 516 TRUE
## 589 TRUE
## sold.fctr.predict.Ensemble.glmnet.err.abs
## 655 0.9939932
## 728 0.9849611
## 555 0.9804967
## 290 0.9653119
## 516 0.9562893
## 589 0.9539285
## sold.fctr.predict.Ensemble.glmnet.accurate
## 655 FALSE
## 728 FALSE
## 555 FALSE
## 290 FALSE
## 516 FALSE
## 589 FALSE
## sold.fctr.predict.Ensemble.glmnet.error
## 655 -0.1939932
## 728 -0.1849611
## 555 -0.1804967
## 290 -0.1653119
## 516 -0.1562893
## 589 -0.1539285
## UniqueID sold.fctr sold.fctr.predict.RFE.X.rpart.prob
## 451 10759 Y 0.9213483
## 786 11700 N 0.9213483
## 400 10683 N 0.9213483
## 459 10774 N 0.3928571
## 416 10705 N 0.3928571
## 800 11718 N 0.9213483
## sold.fctr.predict.RFE.X.rpart sold.fctr.predict.RFE.X.rpart.err
## 451 Y FALSE
## 786 Y TRUE
## 400 Y TRUE
## 459 Y TRUE
## 416 Y TRUE
## 800 Y TRUE
## sold.fctr.predict.RFE.X.rpart.err.abs
## 451 0.07865169
## 786 0.92134831
## 400 0.92134831
## 459 0.39285714
## 416 0.39285714
## 800 0.92134831
## sold.fctr.predict.RFE.X.rpart.accurate
## 451 TRUE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 FALSE
## sold.fctr.predict.RFE.X.earth.prob sold.fctr.predict.RFE.X.earth
## 451 0.7271474 Y
## 786 0.7745526 Y
## 400 0.6572040 Y
## 459 0.8034417 Y
## 416 0.7174067 Y
## 800 0.4863543 N
## sold.fctr.predict.RFE.X.earth.err
## 451 FALSE
## 786 TRUE
## 400 TRUE
## 459 TRUE
## 416 TRUE
## 800 FALSE
## sold.fctr.predict.RFE.X.earth.err.abs
## 451 0.2728526
## 786 0.7745526
## 400 0.6572040
## 459 0.8034417
## 416 0.7174067
## 800 0.4863543
## sold.fctr.predict.RFE.X.earth.accurate
## 451 TRUE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 TRUE
## sold.fctr.predict.RFE.X.glmnet.prob sold.fctr.predict.RFE.X.glmnet
## 451 0.6880155 Y
## 786 0.8516761 Y
## 400 0.7043965 Y
## 459 0.7853686 Y
## 416 0.5114710 Y
## 800 0.3734230 N
## sold.fctr.predict.RFE.X.glmnet.err
## 451 FALSE
## 786 TRUE
## 400 TRUE
## 459 TRUE
## 416 TRUE
## 800 FALSE
## sold.fctr.predict.RFE.X.glmnet.err.abs
## 451 0.3119845
## 786 0.8516761
## 400 0.7043965
## 459 0.7853686
## 416 0.5114710
## 800 0.3734230
## sold.fctr.predict.RFE.X.glmnet.accurate
## 451 TRUE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 TRUE
## sold.fctr.predict.RFE.X.glm.prob sold.fctr.predict.RFE.X.glm
## 451 0.03883274 N
## 786 0.50853277 Y
## 400 0.92804951 Y
## 459 0.84956362 Y
## 416 0.74703459 Y
## 800 0.87448318 Y
## sold.fctr.predict.RFE.X.glm.err sold.fctr.predict.RFE.X.glm.err.abs
## 451 TRUE 0.9611673
## 786 TRUE 0.5085328
## 400 TRUE 0.9280495
## 459 TRUE 0.8495636
## 416 TRUE 0.7470346
## 800 TRUE 0.8744832
## sold.fctr.predict.RFE.X.glm.accurate
## 451 FALSE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 FALSE
## sold.fctr.predict.RFE.X.bayesglm.prob sold.fctr.predict.RFE.X.bayesglm
## 451 0.6039159 Y
## 786 0.7296642 Y
## 400 0.6942100 Y
## 459 0.7709739 Y
## 416 0.6112858 Y
## 800 0.2331726 N
## sold.fctr.predict.RFE.X.bayesglm.err
## 451 FALSE
## 786 TRUE
## 400 TRUE
## 459 TRUE
## 416 TRUE
## 800 FALSE
## sold.fctr.predict.RFE.X.bayesglm.err.abs
## 451 0.3960841
## 786 0.7296642
## 400 0.6942100
## 459 0.7709739
## 416 0.6112858
## 800 0.2331726
## sold.fctr.predict.RFE.X.bayesglm.accurate
## 451 TRUE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 TRUE
## sold.fctr.predict.RFE.X.nnet.prob sold.fctr.predict.RFE.X.nnet
## 451 0.6362631 Y
## 786 0.9276154 Y
## 400 0.7070263 Y
## 459 0.7692427 Y
## 416 0.4507756 Y
## 800 0.1066465 N
## sold.fctr.predict.RFE.X.nnet.err sold.fctr.predict.RFE.X.nnet.err.abs
## 451 FALSE 0.3637369
## 786 TRUE 0.9276154
## 400 TRUE 0.7070263
## 459 TRUE 0.7692427
## 416 TRUE 0.4507756
## 800 FALSE 0.1066465
## sold.fctr.predict.RFE.X.nnet.accurate
## 451 TRUE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 TRUE
## sold.fctr.predict.RFE.X.avNNet.prob sold.fctr.predict.RFE.X.avNNet
## 451 0.7439584 Y
## 786 0.8454229 Y
## 400 0.7435954 Y
## 459 0.7446146 Y
## 416 0.6738188 Y
## 800 0.2621188 N
## sold.fctr.predict.RFE.X.avNNet.err
## 451 FALSE
## 786 TRUE
## 400 TRUE
## 459 TRUE
## 416 TRUE
## 800 FALSE
## sold.fctr.predict.RFE.X.avNNet.err.abs
## 451 0.2560416
## 786 0.8454229
## 400 0.7435954
## 459 0.7446146
## 416 0.6738188
## 800 0.2621188
## sold.fctr.predict.RFE.X.avNNet.accurate
## 451 TRUE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 TRUE
## sold.fctr.predict.RFE.X.gbm.prob sold.fctr.predict.RFE.X.gbm
## 451 0.7664773 Y
## 786 0.9204541 Y
## 400 0.7386111 Y
## 459 0.7417012 Y
## 416 0.7298035 Y
## 800 0.8247307 Y
## sold.fctr.predict.RFE.X.gbm.err sold.fctr.predict.RFE.X.gbm.err.abs
## 451 FALSE 0.2335227
## 786 TRUE 0.9204541
## 400 TRUE 0.7386111
## 459 TRUE 0.7417012
## 416 TRUE 0.7298035
## 800 TRUE 0.8247307
## sold.fctr.predict.RFE.X.gbm.accurate
## 451 TRUE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 FALSE
## sold.fctr.predict.Ensemble.glmnet.prob
## 451 0.1239206
## 786 0.8699366
## 400 0.9423055
## 459 0.9567143
## 416 0.9568462
## 800 0.9828611
## sold.fctr.predict.Ensemble.glmnet
## 451 N
## 786 Y
## 400 Y
## 459 Y
## 416 Y
## 800 Y
## sold.fctr.predict.Ensemble.glmnet.err
## 451 TRUE
## 786 TRUE
## 400 TRUE
## 459 TRUE
## 416 TRUE
## 800 TRUE
## sold.fctr.predict.Ensemble.glmnet.err.abs
## 451 0.8760794
## 786 0.8699366
## 400 0.9423055
## 459 0.9567143
## 416 0.9568462
## 800 0.9828611
## sold.fctr.predict.Ensemble.glmnet.accurate
## 451 FALSE
## 786 FALSE
## 400 FALSE
## 459 FALSE
## 416 FALSE
## 800 FALSE
## sold.fctr.predict.Ensemble.glmnet.error
## 451 -0.07607945
## 786 0.66993660
## 400 0.74230549
## 459 0.75671428
## 416 0.75684620
## 800 0.78286114
## UniqueID sold.fctr sold.fctr.predict.RFE.X.rpart.prob
## 698 11471 N 0.9213483
## 281 10488 N 0.9213483
## 781 11691 N 0.9213483
## 78 10199 N 0.9213483
## 800 11718 N 0.9213483
## 227 10413 N 0.9213483
## sold.fctr.predict.RFE.X.rpart sold.fctr.predict.RFE.X.rpart.err
## 698 Y TRUE
## 281 Y TRUE
## 781 Y TRUE
## 78 Y TRUE
## 800 Y TRUE
## 227 Y TRUE
## sold.fctr.predict.RFE.X.rpart.err.abs
## 698 0.9213483
## 281 0.9213483
## 781 0.9213483
## 78 0.9213483
## 800 0.9213483
## 227 0.9213483
## sold.fctr.predict.RFE.X.rpart.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 FALSE
## 227 FALSE
## sold.fctr.predict.RFE.X.earth.prob sold.fctr.predict.RFE.X.earth
## 698 0.9899425 Y
## 281 0.9787313 Y
## 781 1.0000000 Y
## 78 0.9787313 Y
## 800 0.4863543 N
## 227 0.9232812 Y
## sold.fctr.predict.RFE.X.earth.err
## 698 TRUE
## 281 TRUE
## 781 TRUE
## 78 TRUE
## 800 FALSE
## 227 TRUE
## sold.fctr.predict.RFE.X.earth.err.abs
## 698 0.9899425
## 281 0.9787313
## 781 1.0000000
## 78 0.9787313
## 800 0.4863543
## 227 0.9232812
## sold.fctr.predict.RFE.X.earth.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 TRUE
## 227 FALSE
## sold.fctr.predict.RFE.X.glmnet.prob sold.fctr.predict.RFE.X.glmnet
## 698 0.9956564 Y
## 281 0.9439279 Y
## 781 0.9551653 Y
## 78 0.9604228 Y
## 800 0.3734230 N
## 227 0.9320227 Y
## sold.fctr.predict.RFE.X.glmnet.err
## 698 TRUE
## 281 TRUE
## 781 TRUE
## 78 TRUE
## 800 FALSE
## 227 TRUE
## sold.fctr.predict.RFE.X.glmnet.err.abs
## 698 0.9956564
## 281 0.9439279
## 781 0.9551653
## 78 0.9604228
## 800 0.3734230
## 227 0.9320227
## sold.fctr.predict.RFE.X.glmnet.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 TRUE
## 227 FALSE
## sold.fctr.predict.RFE.X.glm.prob sold.fctr.predict.RFE.X.glm
## 698 0.9952982 Y
## 281 0.9843420 Y
## 781 1.0000000 Y
## 78 0.9984629 Y
## 800 0.8744832 Y
## 227 0.9942633 Y
## sold.fctr.predict.RFE.X.glm.err sold.fctr.predict.RFE.X.glm.err.abs
## 698 TRUE 0.9952982
## 281 TRUE 0.9843420
## 781 TRUE 1.0000000
## 78 TRUE 0.9984629
## 800 TRUE 0.8744832
## 227 TRUE 0.9942633
## sold.fctr.predict.RFE.X.glm.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 FALSE
## 227 FALSE
## sold.fctr.predict.RFE.X.bayesglm.prob sold.fctr.predict.RFE.X.bayesglm
## 698 0.9992746 Y
## 281 0.9704225 Y
## 781 0.9875589 Y
## 78 0.9904310 Y
## 800 0.2331726 N
## 227 0.9616759 Y
## sold.fctr.predict.RFE.X.bayesglm.err
## 698 TRUE
## 281 TRUE
## 781 TRUE
## 78 TRUE
## 800 FALSE
## 227 TRUE
## sold.fctr.predict.RFE.X.bayesglm.err.abs
## 698 0.9992746
## 281 0.9704225
## 781 0.9875589
## 78 0.9904310
## 800 0.2331726
## 227 0.9616759
## sold.fctr.predict.RFE.X.bayesglm.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 TRUE
## 227 FALSE
## sold.fctr.predict.RFE.X.nnet.prob sold.fctr.predict.RFE.X.nnet
## 698 0.9943525 Y
## 281 0.9895618 Y
## 781 0.9887007 Y
## 78 0.9921595 Y
## 800 0.1066465 N
## 227 0.9845457 Y
## sold.fctr.predict.RFE.X.nnet.err sold.fctr.predict.RFE.X.nnet.err.abs
## 698 TRUE 0.9943525
## 281 TRUE 0.9895618
## 781 TRUE 0.9887007
## 78 TRUE 0.9921595
## 800 FALSE 0.1066465
## 227 TRUE 0.9845457
## sold.fctr.predict.RFE.X.nnet.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 TRUE
## 227 FALSE
## sold.fctr.predict.RFE.X.avNNet.prob sold.fctr.predict.RFE.X.avNNet
## 698 0.9616079 Y
## 281 0.9574763 Y
## 781 0.9525829 Y
## 78 0.9608936 Y
## 800 0.2621188 N
## 227 0.9483253 Y
## sold.fctr.predict.RFE.X.avNNet.err
## 698 TRUE
## 281 TRUE
## 781 TRUE
## 78 TRUE
## 800 FALSE
## 227 TRUE
## sold.fctr.predict.RFE.X.avNNet.err.abs
## 698 0.9616079
## 281 0.9574763
## 781 0.9525829
## 78 0.9608936
## 800 0.2621188
## 227 0.9483253
## sold.fctr.predict.RFE.X.avNNet.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 TRUE
## 227 FALSE
## sold.fctr.predict.RFE.X.gbm.prob sold.fctr.predict.RFE.X.gbm
## 698 0.9814251 Y
## 281 0.9640122 Y
## 781 0.9710303 Y
## 78 0.9774973 Y
## 800 0.8247307 Y
## 227 0.9661765 Y
## sold.fctr.predict.RFE.X.gbm.err sold.fctr.predict.RFE.X.gbm.err.abs
## 698 TRUE 0.9814251
## 281 TRUE 0.9640122
## 781 TRUE 0.9710303
## 78 TRUE 0.9774973
## 800 TRUE 0.8247307
## 227 TRUE 0.9661765
## sold.fctr.predict.RFE.X.gbm.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 FALSE
## 227 FALSE
## sold.fctr.predict.Ensemble.glmnet.prob
## 698 0.9795388
## 281 0.9812620
## 781 0.9814704
## 78 0.9820195
## 800 0.9828611
## 227 0.9831472
## sold.fctr.predict.Ensemble.glmnet
## 698 Y
## 281 Y
## 781 Y
## 78 Y
## 800 Y
## 227 Y
## sold.fctr.predict.Ensemble.glmnet.err
## 698 TRUE
## 281 TRUE
## 781 TRUE
## 78 TRUE
## 800 TRUE
## 227 TRUE
## sold.fctr.predict.Ensemble.glmnet.err.abs
## 698 0.9795388
## 281 0.9812620
## 781 0.9814704
## 78 0.9820195
## 800 0.9828611
## 227 0.9831472
## sold.fctr.predict.Ensemble.glmnet.accurate
## 698 FALSE
## 281 FALSE
## 781 FALSE
## 78 FALSE
## 800 FALSE
## 227 FALSE
## sold.fctr.predict.Ensemble.glmnet.error
## 698 0.7795388
## 281 0.7812620
## 781 0.7814704
## 78 0.7820195
## 800 0.7828611
## 227 0.7831472
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df = glb_fitobs_df, mdl_id = glb_sel_mdl_id, label = "fit"),
by = glb_category_var, all = TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id, label="OOB"),
#by=glb_category_var, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))## prdl.descr.my.fctr .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## iPadmini3#1 iPadmini3#1 4 3 0.007978723 0.009259259
## iPadmini2#1 iPadmini2#1 15 13 0.034574468 0.034722222
## iPadmini#1 iPadmini#1 24 20 0.053191489 0.055555556
## iPadmini3#0 iPadmini3#0 13 11 0.029255319 0.030092593
## Unknown#1 Unknown#1 28 24 0.063829787 0.064814815
## Unknown#0 Unknown#0 16 14 0.037234043 0.037037037
## iPad1#1 iPad1#1 23 20 0.053191489 0.053240741
## iPad3#1 iPad3#1 7 5 0.013297872 0.016203704
## iPad4#1 iPad4#1 17 15 0.039893617 0.039351852
## iPad4#0 iPad4#0 13 11 0.029255319 0.030092593
## iPadmini2#0 iPadmini2#0 24 21 0.055851064 0.055555556
## iPad2#1 iPad2#1 21 18 0.047872340 0.048611111
## iPadAir2#0 iPadAir2#0 21 19 0.050531915 0.048611111
## iPadmini#0 iPadmini#0 44 39 0.103723404 0.101851852
## iPad3#0 iPad3#0 21 18 0.047872340 0.048611111
## iPadAir#1 iPadAir#1 16 14 0.037234043 0.037037037
## iPad1#0 iPad1#0 34 30 0.079787234 0.078703704
## iPad2#0 iPad2#0 58 52 0.138297872 0.134259259
## iPadAir#0 iPadAir#0 25 22 0.058510638 0.057870370
## iPadAir2#1 iPadAir2#1 8 7 0.018617021 0.018518519
## err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## iPadmini3#1 0.01122104 0.01122104 1 1.709065
## iPadmini2#1 0.03149610 0.01574805 2 6.240199
## iPadmini#1 2.39469879 0.07982329 30 6.903829
## iPadmini3#0 0.54235118 0.04519593 12 3.539873
## Unknown#1 0.05844056 0.01461014 4 7.020221
## Unknown#0 6.78540300 0.18848342 36 3.802775
## iPad1#1 3.57274702 0.12759811 28 5.415330
## iPad3#1 1.68087992 0.09338222 18 1.607253
## iPad4#1 0.75385034 0.08376115 9 3.777315
## iPad4#0 1.51381325 0.06055253 25 2.748442
## iPadmini2#0 0.45051170 0.09010234 5 5.047830
## iPad2#1 2.48585383 0.06905150 36 4.255439
## iPadAir2#0 4.43222740 0.16415657 27 3.981589
## iPadmini#0 5.99909945 0.15787104 38 8.083408
## iPad3#0 3.59127017 0.11584742 31 3.834528
## iPadAir#1 1.23704849 0.06872492 18 2.862180
## iPad1#0 1.66067119 0.05032337 33 6.065565
## iPad2#0 2.30394852 0.09215794 25 10.130293
## iPadAir#0 0.76215620 0.05081041 15 3.903209
## iPadAir2#1 1.48628213 0.14862821 10 1.002698
## err.abs.OOB.mean
## iPadmini3#1 0.4272664
## iPadmini2#1 0.4160133
## iPadmini#1 0.2876595
## iPadmini3#0 0.2722979
## Unknown#1 0.2507222
## Unknown#0 0.2376734
## iPad1#1 0.2354491
## iPad3#1 0.2296076
## iPad4#1 0.2221950
## iPad4#0 0.2114186
## iPadmini2#0 0.2103262
## iPad2#1 0.2026400
## iPadAir2#0 0.1895995
## iPadmini#0 0.1837138
## iPad3#0 0.1825966
## iPadAir#1 0.1788862
## iPad1#0 0.1783990
## iPad2#0 0.1746602
## iPadAir#0 0.1561283
## iPadAir2#1 0.1253372print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))## .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## 432.00000 376.00000 1.00000 1.00000
## err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## 41.75397 1.72805 403.00000 91.93104
## err.abs.OOB.mean
## 4.57259write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 12 fit.models 6 2 2 459.616 532.107 72.492
## 13 fit.models 6 3 3 532.108 NA NA# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb_to_sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glb_fitobs_df), names(glb_trnobs_df)))
glb_trnobs_df[glb_trnobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
for (col in setdiff(names(glb_fitobs_df), names(glb_allobs_df)))
glb_allobs_df[glb_allobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
if (all(is.na(glb_newobs_df[, glb_rsp_var])))
for (col in setdiff(names(glb_OOBobs_df), names(glb_trnobs_df)))
glb_trnobs_df[glb_trnobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
}
sync_glb_obs_df()
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 13 fit.models 6 3 3 532.108 541.552
## 14 fit.data.training 7 0 0 541.552 NA
## elapsed
## 13 9.444
## 14 NAStep 7.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
#stop(here"); glb_to_sav()
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else if (nrow(glb_fitobs_df) + length(glb_obsfit_outliers) == nrow(glb_trnobs_df))
# This condition should be for checking NAs for glb_rsp_var in glb_newobs_df instead ?
# if (!any(is.na(glb_newobs_df[, glb_rsp_var])))
{
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {
if (grepl("RFE", glb_sel_mdl_id) ||
(!is.null(glb_mdl_ensemble) && grepl("RFE", glb_mdl_ensemble))) {
indep_vars <- myadjust_interaction_feats(subset(glb_feats_df,
!nzv & (exclude.as.feat != 1))[, "id"])
rfe_trn_results <- myrun_rfe(glb_trnobs_df, indep_vars, glb_rfe_fit_sizes)
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
# Fit selected models on glb_trnobs_df
for (mdl_id in gsub(".prob", "",
gsub(glb_rsp_var_out, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjust_interaction_feats(myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glb_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indep_vars = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glb_trnobs_df, OOB_df = NULL)
glb_trnobs_df <- glb_get_predictions(df = glb_trnobs_df,
mdl_id = tail(glb_models_df$id, 1),
rsp_var_out = glb_rsp_var_out,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glb_newobs_df <- glb_get_predictions(df = glb_newobs_df,
mdl_id = tail(glb_models_df$id, 1),
rsp_var_out = glb_rsp_var_out,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
print("***************")
print("Outliers in obsTrnDF not deleted yet")
print("***************")
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
# Discontinuing use of tune_finmdl_df;
# since final model needs to be cved on glb_trnobs_df
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
fit_trnobs_df <- if (is.null(glb_obstrn_outliers)) glb_trnobs_df else
glb_trnobs_df[!(glb_trnobs_df[, glb_id_var] %in% glb_obstrn_outliers), ]
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c("glm", tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indep_vars_vctr) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = ifelse(grepl("Ensemble", glb_sel_mdl_id),
"Final.Ensemble", "Final"),
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indep_vars = indep_vars_vctr, rsp_var = glb_rsp_var,
fit_df = fit_trnobs_df, OOB_df = NULL)
}
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 47 0.8515 0.5507 0.02376 0.07039
## 48 0.8483 0.5388 0.02399 0.07066
## 49 0.8495 0.5465 0.02277 0.06825
## 50 0.8483 0.5438 0.02525 0.07096
## 51 0.8495 0.5455 0.02262 0.06774
## 52 0.8503 0.5480 0.02257 0.06674
## 53 0.8511 0.5500 0.02396 0.06903
## 54 0.8499 0.5448 0.02398 0.07031
## 82 0.8547 0.5573 0.02189 0.06689 *
##
## The top 5 variables (out of 82):
## spdiff.cut.fctr(-1e+03,-100], sprice.log10, sprice.root2, sprice.d20nexp, spdiff.cut.fctr(10,100]
##
## [1] "spdiff.cut.fctr(-1e+03,-100]"
## [2] "sprice.log10"
## [3] "sprice.root2"
## [4] "sprice.d20nexp"
## [5] "spdiff.cut.fctr(10,100]"
## [6] "spdiff.cut.fctr(-100,-10]"
## [7] "prdl.descr.my.fctriPad2#0"
## [8] "spdiff.cut.fctr(100,1e+03]"
## [9] "startprice.dgt1.is9"
## [10] "prdl.descr.my.fctriPad1#0"
## [11] "spdiff.cut.fctr(-1,0]"
## [12] "condition.fctrSeller refurbished"
## [13] "cellular.fctrUnknown"
## [14] "cellular.fctrUnknown:carrier.fctrUnknown"
## [15] "startprice.dcm2.is9"
## [16] "spdiff.cut.fctr(1,10]"
## [17] "D.weight.sum.stem.stop.Ratio"
## [18] "D.wrds.unq.n.log"
## [19] "D.terms.post.stem.n.log"
## [20] "D.ratio.wrds.stop.n.wrds.n"
## [21] "D.terms.post.stop.n.log"
## [22] "spdiff.cut.fctr(-10,-1]"
## [23] "D.wrds.n.log"
## [24] "D.chrs.n.log"
## [25] "startprice.dcm1.is9"
## [26] "prdl.descr.my.fctriPad1#1"
## [27] "startprice.dgt2.is9"
## [28] "color.fctrSpace Gray"
## [29] "prdl.descr.my.fctriPadAir2#0"
## [30] "condition.fctrFor parts or not working"
## [31] "D.weight.post.stem.sum"
## [32] "color.fctrUnknown"
## [33] "spdiff.cut.fctr(0,1]"
## [34] "D.weight.post.stop.sum"
## [35] "prdl.descr.my.fctriPadAir#0"
## [36] "prdl.descr.my.fctriPad3#0"
## [37] "D.weight.sum"
## [38] "storage.fctrUnknown"
## [39] "cellular.fctr0:carrier.fctrNone"
## [40] "prdl.descr.my.fctriPadmini#1"
## [41] "D.chrs.pnct13.n.log"
## [42] "D.chrs.pnct11.n.log"
## [43] "D.wrds.stop.n.log"
## [44] "condition.fctrNew"
## [45] ".rnorm"
## [46] "prdl.descr.my.fctriPadmini#0"
## [47] "prdl.descr.my.fctrUnknown#1"
## [48] "prdl.descr.my.fctriPadAir2#1"
## [49] "prdl.descr.my.fctriPad4#0"
## [50] "storage.fctr64"
## [51] "D.ratio.weight.sum.wrds.n"
## [52] "storage.fctr32"
## [53] "color.fctrWhite"
## [54] "prdl.descr.my.fctriPad2#1"
## [55] "prdl.descr.my.fctriPad4#1"
## [56] "storage.fctr16"
## [57] "cellular.fctr1:carrier.fctrT-Mobile"
## [58] "prdl.descr.my.fctriPadAir#1"
## [59] "cellular.fctr1:carrier.fctrVerizon"
## [60] "cellular.fctr1"
## [61] "condition.fctrManufacturer refurbished"
## [62] "cellular.fctr1:carrier.fctrUnknown"
## [63] "color.fctrGold"
## [64] "prdl.descr.my.fctriPadmini3#1"
## [65] "prdl.descr.my.fctriPad3#1"
## [66] "cellular.fctr1:carrier.fctrOther"
## [67] "prdl.descr.my.fctriPadmini2#1"
## [68] "cellular.fctr0:carrier.fctrOther"
## [69] "cellular.fctr0:carrier.fctrSprint"
## [70] "cellular.fctr0:carrier.fctrT-Mobile"
## [71] "cellular.fctr0:carrier.fctrUnknown"
## [72] "cellular.fctr0:carrier.fctrVerizon"
## [73] "cellular.fctr1:carrier.fctrNone"
## [74] "cellular.fctrUnknown:carrier.fctrNone"
## [75] "cellular.fctrUnknown:carrier.fctrOther"
## [76] "cellular.fctrUnknown:carrier.fctrSprint"
## [77] "cellular.fctrUnknown:carrier.fctrT-Mobile"
## [78] "cellular.fctrUnknown:carrier.fctrVerizon"
## [79] "prdl.descr.my.fctriPadmini3#0"
## [80] "condition.fctrNew other (see details)"
## [81] "cellular.fctr1:carrier.fctrSprint"
## [82] "prdl.descr.my.fctriPadmini2#0"## [1] "fitting model: RFE.X.Train.gbm"
## [1] " indep_vars: spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting n.trees = 100, interaction.depth = 4, shrinkage = 0.1, n.minobsinnode = 10 on full training set## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 65: cellular.fctr1:carrier.fctrNone has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 66: cellular.fctrUnknown:carrier.fctrNone has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 67: cellular.fctr0:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 69: cellular.fctrUnknown:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 70: cellular.fctr0:carrier.fctrSprint has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 72: cellular.fctrUnknown:carrier.fctrSprint has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 73: cellular.fctr0:carrier.fctrT-Mobile has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 75: cellular.fctrUnknown:carrier.fctrT-Mobile has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 76: cellular.fctr0:carrier.fctrUnknown has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 79: cellular.fctr0:carrier.fctrVerizon has no variation.## Warning in gbm.fit(x = structure(c(-0.480112420809766, 1.43504225194153, :
## variable 81: cellular.fctrUnknown:carrier.fctrVerizon has no variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0292 nan 0.1000 0.0314
## 2 0.9823 nan 0.1000 0.0193
## 3 0.9365 nan 0.1000 0.0160
## 4 0.9100 nan 0.1000 0.0118
## 5 0.8884 nan 0.1000 0.0060
## 6 0.8650 nan 0.1000 0.0096
## 7 0.8436 nan 0.1000 0.0090
## 8 0.8269 nan 0.1000 0.0056
## 9 0.8138 nan 0.1000 0.0049
## 10 0.7997 nan 0.1000 0.0054
## 20 0.7126 nan 0.1000 0.0028
## 40 0.6299 nan 0.1000 -0.0002
## 60 0.5700 nan 0.1000 0.0000
## 80 0.5266 nan 0.1000 0.0002
## 100 0.4912 nan 0.1000 -0.0036## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: shrinkage## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: n.minobsinnode
## var
## sprice.log10 sprice.log10
## sprice.d20nexp sprice.d20nexp
## .rnorm .rnorm
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(10,100]
## spdiff.cut.fctr(-100,-10] spdiff.cut.fctr(-100,-10]
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#0
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n
## condition.fctrFor parts or not working condition.fctrFor parts or not working
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n
## spdiff.cut.fctr(100,1e+03] spdiff.cut.fctr(100,1e+03]
## cellular.fctrUnknown cellular.fctrUnknown
## color.fctrUnknown color.fctrUnknown
## D.weight.post.stop.sum D.weight.post.stop.sum
## prdl.descr.my.fctriPad1#0 prdl.descr.my.fctriPad1#0
## storage.fctrUnknown storage.fctrUnknown
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-10,-1]
## D.weight.post.stem.sum D.weight.post.stem.sum
## color.fctrSpace Gray color.fctrSpace Gray
## prdl.descr.my.fctriPad3#0 prdl.descr.my.fctriPad3#0
## cellular.fctr0:carrier.fctrNone cellular.fctr0:carrier.fctrNone
## startprice.dcm1.is9 startprice.dcm1.is9
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#0
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#0
## prdl.descr.my.fctriPadAir#1 prdl.descr.my.fctriPadAir#1
## D.wrds.n.log D.wrds.n.log
## prdl.descr.my.fctriPadAir2#1 prdl.descr.my.fctriPadAir2#1
## D.chrs.n.log D.chrs.n.log
## spdiff.cut.fctr(1,10] spdiff.cut.fctr(1,10]
## startprice.dcm2.is9 startprice.dcm2.is9
## D.wrds.stop.n.log D.wrds.stop.n.log
## cellular.fctr1 cellular.fctr1
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctrUnknown#1
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log
## startprice.dgt1.is9 startprice.dgt1.is9
## D.terms.post.stop.n.log D.terms.post.stop.n.log
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad1#1
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log
## storage.fctr16 storage.fctr16
## condition.fctrNew condition.fctrNew
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#0
## color.fctrWhite color.fctrWhite
## prdl.descr.my.fctriPad2#0 prdl.descr.my.fctriPad2#0
## prdl.descr.my.fctriPadmini#1 prdl.descr.my.fctriPadmini#1
## condition.fctrSeller refurbished condition.fctrSeller refurbished
## storage.fctr64 storage.fctr64
## spdiff.cut.fctr(-1,0] spdiff.cut.fctr(-1,0]
## D.terms.post.stem.n.log D.terms.post.stem.n.log
## startprice.dgt2.is9 startprice.dgt2.is9
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#0
## cellular.fctr1:carrier.fctrVerizon cellular.fctr1:carrier.fctrVerizon
## color.fctrGold color.fctrGold
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#0
## D.weight.sum D.weight.sum
## D.wrds.unq.n.log D.wrds.unq.n.log
## condition.fctrManufacturer refurbished condition.fctrManufacturer refurbished
## condition.fctrNew other (see details) condition.fctrNew other (see details)
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad2#1
## prdl.descr.my.fctriPad3#1 prdl.descr.my.fctriPad3#1
## prdl.descr.my.fctriPad4#1 prdl.descr.my.fctriPad4#1
## prdl.descr.my.fctriPadmini2#1 prdl.descr.my.fctriPadmini2#1
## prdl.descr.my.fctriPadmini3#1 prdl.descr.my.fctriPadmini3#1
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(0,1]
## sprice.root2 sprice.root2
## storage.fctr32 storage.fctr32
## cellular.fctr1:carrier.fctrNone cellular.fctr1:carrier.fctrNone
## cellular.fctrUnknown:carrier.fctrNone cellular.fctrUnknown:carrier.fctrNone
## cellular.fctr0:carrier.fctrOther cellular.fctr0:carrier.fctrOther
## cellular.fctr1:carrier.fctrOther cellular.fctr1:carrier.fctrOther
## cellular.fctrUnknown:carrier.fctrOther cellular.fctrUnknown:carrier.fctrOther
## cellular.fctr0:carrier.fctrSprint cellular.fctr0:carrier.fctrSprint
## cellular.fctr1:carrier.fctrSprint cellular.fctr1:carrier.fctrSprint
## cellular.fctrUnknown:carrier.fctrSprint cellular.fctrUnknown:carrier.fctrSprint
## cellular.fctr0:carrier.fctrT-Mobile cellular.fctr0:carrier.fctrT-Mobile
## cellular.fctr1:carrier.fctrT-Mobile cellular.fctr1:carrier.fctrT-Mobile
## cellular.fctrUnknown:carrier.fctrT-Mobile cellular.fctrUnknown:carrier.fctrT-Mobile
## cellular.fctr0:carrier.fctrUnknown cellular.fctr0:carrier.fctrUnknown
## cellular.fctr1:carrier.fctrUnknown cellular.fctr1:carrier.fctrUnknown
## cellular.fctrUnknown:carrier.fctrUnknown cellular.fctrUnknown:carrier.fctrUnknown
## cellular.fctr0:carrier.fctrVerizon cellular.fctr0:carrier.fctrVerizon
## cellular.fctrUnknown:carrier.fctrVerizon cellular.fctrUnknown:carrier.fctrVerizon
## rel.inf
## sprice.log10 26.63654519
## sprice.d20nexp 12.81942599
## .rnorm 11.94435485
## spdiff.cut.fctr(10,100] 7.74856031
## spdiff.cut.fctr(-100,-10] 6.41362087
## prdl.descr.my.fctriPadAir2#0 2.81299473
## D.ratio.wrds.stop.n.wrds.n 2.15787858
## condition.fctrFor parts or not working 1.77173306
## D.ratio.weight.sum.wrds.n 1.65296530
## spdiff.cut.fctr(100,1e+03] 1.52746507
## cellular.fctrUnknown 1.36194795
## color.fctrUnknown 1.28885204
## D.weight.post.stop.sum 1.28590227
## prdl.descr.my.fctriPad1#0 1.17349631
## storage.fctrUnknown 1.08734811
## spdiff.cut.fctr(-10,-1] 1.06858726
## D.weight.post.stem.sum 1.05410089
## color.fctrSpace Gray 0.96468338
## prdl.descr.my.fctriPad3#0 0.93529080
## cellular.fctr0:carrier.fctrNone 0.92658142
## startprice.dcm1.is9 0.89593767
## D.weight.sum.stem.stop.Ratio 0.81691626
## prdl.descr.my.fctriPadAir#0 0.80944952
## prdl.descr.my.fctriPadmini#0 0.71670380
## prdl.descr.my.fctriPadAir#1 0.62783397
## D.wrds.n.log 0.61972999
## prdl.descr.my.fctriPadAir2#1 0.60692053
## D.chrs.n.log 0.59650780
## spdiff.cut.fctr(1,10] 0.59198557
## startprice.dcm2.is9 0.58348722
## D.wrds.stop.n.log 0.49079878
## cellular.fctr1 0.46968098
## prdl.descr.my.fctrUnknown#1 0.46071058
## D.chrs.pnct13.n.log 0.44829294
## startprice.dgt1.is9 0.43009395
## D.terms.post.stop.n.log 0.42459325
## prdl.descr.my.fctriPad1#1 0.41412210
## D.chrs.pnct11.n.log 0.38841370
## storage.fctr16 0.36308629
## condition.fctrNew 0.31318088
## prdl.descr.my.fctriPad4#0 0.30203076
## color.fctrWhite 0.28168763
## prdl.descr.my.fctriPad2#0 0.26469103
## prdl.descr.my.fctriPadmini#1 0.22537307
## condition.fctrSeller refurbished 0.20871634
## storage.fctr64 0.19313711
## spdiff.cut.fctr(-1,0] 0.17848181
## D.terms.post.stem.n.log 0.16482427
## startprice.dgt2.is9 0.16081161
## prdl.descr.my.fctriPadmini2#0 0.09381928
## cellular.fctr1:carrier.fctrVerizon 0.08748816
## color.fctrGold 0.06983728
## prdl.descr.my.fctriPadmini3#0 0.06832147
## D.weight.sum 0.00000000
## D.wrds.unq.n.log 0.00000000
## condition.fctrManufacturer refurbished 0.00000000
## condition.fctrNew other (see details) 0.00000000
## prdl.descr.my.fctriPad2#1 0.00000000
## prdl.descr.my.fctriPad3#1 0.00000000
## prdl.descr.my.fctriPad4#1 0.00000000
## prdl.descr.my.fctriPadmini2#1 0.00000000
## prdl.descr.my.fctriPadmini3#1 0.00000000
## spdiff.cut.fctr(0,1] 0.00000000
## sprice.root2 0.00000000
## storage.fctr32 0.00000000
## cellular.fctr1:carrier.fctrNone 0.00000000
## cellular.fctrUnknown:carrier.fctrNone 0.00000000
## cellular.fctr0:carrier.fctrOther 0.00000000
## cellular.fctr1:carrier.fctrOther 0.00000000
## cellular.fctrUnknown:carrier.fctrOther 0.00000000
## cellular.fctr0:carrier.fctrSprint 0.00000000
## cellular.fctr1:carrier.fctrSprint 0.00000000
## cellular.fctrUnknown:carrier.fctrSprint 0.00000000
## cellular.fctr0:carrier.fctrT-Mobile 0.00000000
## cellular.fctr1:carrier.fctrT-Mobile 0.00000000
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.00000000
## cellular.fctr0:carrier.fctrUnknown 0.00000000
## cellular.fctr1:carrier.fctrUnknown 0.00000000
## cellular.fctrUnknown:carrier.fctrUnknown 0.00000000
## cellular.fctr0:carrier.fctrVerizon 0.00000000
## cellular.fctrUnknown:carrier.fctrVerizon 0.00000000
## sold.fctr sold.fctr.predict.RFE.X.Train.gbm.N
## 1 N 147
## 2 Y 17
## sold.fctr.predict.RFE.X.Train.gbm.Y
## 1 50
## 2 621
## Prediction
## Reference N Y
## N 147 50
## Y 17 621
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.197605e-01 7.637647e-01 8.992163e-01 9.372770e-01 7.640719e-01
## AccuracyPValue McnemarPValue
## 2.374050e-32 9.251714e-05
## id
## 1 RFE.X.Train.gbm
## feats
## 1 spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 8.486 0.551
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8597736 0.7461929 0.9733542 0.9532406
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9488159 0.8291082
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8992163 0.937277 0.4925704
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01892634 0.05638583## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## [1] "fitting model: RFE.X.Train.glm"
## [1] " indep_vars: spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr"
## + Fold1.Rep1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.02942 0.00005 0.18936 0.45886 2.84236
##
## Coefficients: (15 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 19.98712 60.93929 0.328
## .rnorm -0.19927 0.12604 -1.581
## D.chrs.n.log 0.95212 1.86224 0.511
## D.chrs.pnct11.n.log 0.26515 0.61757 0.429
## D.chrs.pnct13.n.log -0.14629 0.51461 -0.284
## D.ratio.weight.sum.wrds.n 0.04062 0.51550 0.079
## D.ratio.wrds.stop.n.wrds.n -9.73814 6.57833 -1.480
## D.terms.post.stem.n.log -1.02229 12.53277 -0.082
## D.terms.post.stop.n.log -4.19973 12.98078 -0.324
## D.weight.post.stem.sum -0.01655 10.64163 -0.002
## D.weight.post.stop.sum 0.35810 10.41186 0.034
## D.weight.sum NA NA NA
## D.weight.sum.stem.stop.Ratio -3.53675 60.42403 -0.059
## D.wrds.n.log 2.49549 3.05199 0.818
## D.wrds.stop.n.log 1.20606 1.02228 1.180
## D.wrds.unq.n.log NA NA NA
## cellular.fctr1 0.11483 0.47891 0.240
## cellular.fctrUnknown -0.46705 0.61557 -0.759
## color.fctrGold 0.21642 0.93471 0.232
## `color.fctrSpace Gray` -0.05873 0.51013 -0.115
## color.fctrUnknown -0.38313 0.37155 -1.031
## color.fctrWhite -0.44725 0.38921 -1.149
## `condition.fctrFor parts or not working` -1.13162 0.44100 -2.566
## `condition.fctrManufacturer refurbished` -0.65171 1.08564 -0.600
## condition.fctrNew 0.27848 0.55776 0.499
## `condition.fctrNew other (see details)` -0.09391 0.79741 -0.118
## `condition.fctrSeller refurbished` -0.97285 0.67336 -1.445
## `prdl.descr.my.fctrUnknown#1` -7.70865 5.73090 -1.345
## `prdl.descr.my.fctriPad1#0` -1.02556 0.76452 -1.341
## `prdl.descr.my.fctriPad1#1` -9.63852 5.70529 -1.689
## `prdl.descr.my.fctriPad2#0` 0.03684 0.68863 0.054
## `prdl.descr.my.fctriPad2#1` -8.52855 5.60327 -1.522
## `prdl.descr.my.fctriPad3#0` 1.46917 0.79410 1.850
## `prdl.descr.my.fctriPad3#1` -8.07432 5.71165 -1.414
## `prdl.descr.my.fctriPad4#0` 0.91786 0.87714 1.046
## `prdl.descr.my.fctriPad4#1` -8.43680 5.76241 -1.464
## `prdl.descr.my.fctriPadAir#0` 2.76711 0.93139 2.971
## `prdl.descr.my.fctriPadAir#1` -6.32920 5.77930 -1.095
## `prdl.descr.my.fctriPadAir2#0` 3.00856 1.03065 2.919
## `prdl.descr.my.fctriPadAir2#1` -4.24200 5.60702 -0.757
## `prdl.descr.my.fctriPadmini#0` -0.07933 0.68966 -0.115
## `prdl.descr.my.fctriPadmini#1` -8.89783 5.75393 -1.546
## `prdl.descr.my.fctriPadmini2#0` 0.70975 0.86855 0.817
## `prdl.descr.my.fctriPadmini2#1` -7.41208 5.75115 -1.289
## `prdl.descr.my.fctriPadmini3#0` 2.07708 1.08768 1.910
## `prdl.descr.my.fctriPadmini3#1` -6.67653 6.01899 -1.109
## `spdiff.cut.fctr(-100,-10]` 2.02512 0.43318 4.675
## `spdiff.cut.fctr(-10,-1]` 3.21550 0.60289 5.333
## `spdiff.cut.fctr(-1,0]` 20.13067 562.02365 0.036
## `spdiff.cut.fctr(0,1]` 5.45380 1.33840 4.075
## `spdiff.cut.fctr(1,10]` 3.70282 0.69004 5.366
## `spdiff.cut.fctr(10,100]` 3.13212 0.54509 5.746
## `spdiff.cut.fctr(100,1e+03]` 3.11857 1.20956 2.578
## sprice.d20nexp -5.93289 2.14038 -2.772
## sprice.log10 -0.70014 0.50113 -1.397
## sprice.root2 -0.25991 0.09822 -2.646
## startprice.dcm1.is9 -0.65237 0.56349 -1.158
## startprice.dcm2.is9 0.35899 0.57013 0.630
## startprice.dgt1.is9 0.34327 0.42187 0.814
## startprice.dgt2.is9 -0.04588 0.45227 -0.101
## storage.fctr16 -1.53192 0.80500 -1.903
## storage.fctr32 -1.19165 0.82398 -1.446
## storage.fctr64 -1.09037 0.80190 -1.360
## storage.fctrUnknown -1.59675 1.02107 -1.564
## `cellular.fctr0:carrier.fctrNone` NA NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA NA
## `cellular.fctr0:carrier.fctrOther` NA NA NA
## `cellular.fctr1:carrier.fctrOther` 16.48540 3978.50928 0.004
## `cellular.fctrUnknown:carrier.fctrOther` NA NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA NA
## `cellular.fctr1:carrier.fctrSprint` 1.96095 1.77298 1.106
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` -0.27880 1.28651 -0.217
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA NA
## `cellular.fctr1:carrier.fctrUnknown` 0.35904 0.69458 0.517
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA NA
## `cellular.fctr1:carrier.fctrVerizon` -0.25071 0.68796 -0.364
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA NA
## Pr(>|z|)
## (Intercept) 0.74292
## .rnorm 0.11387
## D.chrs.n.log 0.60916
## D.chrs.pnct11.n.log 0.66768
## D.chrs.pnct13.n.log 0.77619
## D.ratio.weight.sum.wrds.n 0.93720
## D.ratio.wrds.stop.n.wrds.n 0.13878
## D.terms.post.stem.n.log 0.93499
## D.terms.post.stop.n.log 0.74629
## D.weight.post.stem.sum 0.99876
## D.weight.post.stop.sum 0.97256
## D.weight.sum NA
## D.weight.sum.stem.stop.Ratio 0.95332
## D.wrds.n.log 0.41355
## D.wrds.stop.n.log 0.23809
## D.wrds.unq.n.log NA
## cellular.fctr1 0.81050
## cellular.fctrUnknown 0.44802
## color.fctrGold 0.81690
## `color.fctrSpace Gray` 0.90834
## color.fctrUnknown 0.30247
## color.fctrWhite 0.25050
## `condition.fctrFor parts or not working` 0.01029 *
## `condition.fctrManufacturer refurbished` 0.54830
## condition.fctrNew 0.61758
## `condition.fctrNew other (see details)` 0.90625
## `condition.fctrSeller refurbished` 0.14852
## `prdl.descr.my.fctrUnknown#1` 0.17859
## `prdl.descr.my.fctriPad1#0` 0.17977
## `prdl.descr.my.fctriPad1#1` 0.09114 .
## `prdl.descr.my.fctriPad2#0` 0.95733
## `prdl.descr.my.fctriPad2#1` 0.12799
## `prdl.descr.my.fctriPad3#0` 0.06430 .
## `prdl.descr.my.fctriPad3#1` 0.15746
## `prdl.descr.my.fctriPad4#0` 0.29537
## `prdl.descr.my.fctriPad4#1` 0.14316
## `prdl.descr.my.fctriPadAir#0` 0.00297 **
## `prdl.descr.my.fctriPadAir#1` 0.27345
## `prdl.descr.my.fctriPadAir2#0` 0.00351 **
## `prdl.descr.my.fctriPadAir2#1` 0.44932
## `prdl.descr.my.fctriPadmini#0` 0.90842
## `prdl.descr.my.fctriPadmini#1` 0.12201
## `prdl.descr.my.fctriPadmini2#0` 0.41383
## `prdl.descr.my.fctriPadmini2#1` 0.19747
## `prdl.descr.my.fctriPadmini3#0` 0.05618 .
## `prdl.descr.my.fctriPadmini3#1` 0.26733
## `spdiff.cut.fctr(-100,-10]` 2.94e-06 ***
## `spdiff.cut.fctr(-10,-1]` 9.63e-08 ***
## `spdiff.cut.fctr(-1,0]` 0.97143
## `spdiff.cut.fctr(0,1]` 4.60e-05 ***
## `spdiff.cut.fctr(1,10]` 8.04e-08 ***
## `spdiff.cut.fctr(10,100]` 9.13e-09 ***
## `spdiff.cut.fctr(100,1e+03]` 0.00993 **
## sprice.d20nexp 0.00557 **
## sprice.log10 0.16238
## sprice.root2 0.00814 **
## startprice.dcm1.is9 0.24697
## startprice.dcm2.is9 0.52891
## startprice.dgt1.is9 0.41583
## startprice.dgt2.is9 0.91921
## storage.fctr16 0.05704 .
## storage.fctr32 0.14812
## storage.fctr64 0.17391
## storage.fctrUnknown 0.11786
## `cellular.fctr0:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr0:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrOther` 0.99669
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr0:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrSprint` 0.26872
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.82843
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr0:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrUnknown` 0.60522
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr0:carrier.fctrVerizon` NA
## `cellular.fctr1:carrier.fctrVerizon` 0.71555
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 912.39 on 834 degrees of freedom
## Residual deviance: 471.48 on 768 degrees of freedom
## AIC: 605.48
##
## Number of Fisher Scoring iterations: 17## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.RFE.X.Train.glm.N
## 1 N 130
## 2 Y 33
## sold.fctr.predict.RFE.X.Train.glm.Y
## 1 67
## 2 605
## Prediction
## Reference N Y
## N 130 67
## Y 33 605
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.802395e-01 6.467522e-01 8.562666e-01 9.014876e-01 7.640719e-01
## AccuracyPValue McnemarPValue
## 1.398830e-17 9.668483e-04
## id
## 1 RFE.X.Train.glm
## feats
## 1 spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.37 0.143
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8040872 0.6598985 0.9482759 0.9244307
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9236641 0.8303159
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8562666 0.9014876 0.5050239
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01816809 0.04601597## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## [1] "fitting model: RFE.X.Train.nnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting size = 1, decay = 0.1 on full training set
## # weights: 84
## initial value 505.392304
## iter 10 value 421.172133
## iter 20 value 346.978727
## iter 30 value 281.357087
## iter 40 value 256.209431
## iter 50 value 249.172123
## iter 60 value 248.262755
## iter 70 value 248.200168
## iter 80 value 248.199573
## iter 80 value 248.199571
## iter 80 value 248.199571
## final value 248.199571
## converged## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: decay
## a 81-1-1 network with 84 weights
## options were - entropy fitting decay=0.1
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1
## -1.93 0.15 -1.02 -0.50 -0.03 0.40 -0.24 0.45 0.51
## i9->h1 i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 i17->h1
## 0.44 -0.85 0.44 -1.66 -0.04 -0.18 0.45 -0.91 -0.18
## i18->h1 i19->h1 i20->h1 i21->h1 i22->h1 i23->h1 i24->h1 i25->h1 i26->h1
## -0.53 -0.31 0.09 0.25 1.51 0.30 -0.33 0.10 1.04
## i27->h1 i28->h1 i29->h1 i30->h1 i31->h1 i32->h1 i33->h1 i34->h1 i35->h1
## -0.86 1.21 1.95 -0.02 0.88 -1.21 0.17 -1.26 0.71
## i36->h1 i37->h1 i38->h1 i39->h1 i40->h1 i41->h1 i42->h1 i43->h1 i44->h1
## -1.98 -0.69 -2.55 -2.28 0.06 0.88 -0.91 -0.28 -1.34
## i45->h1 i46->h1 i47->h1 i48->h1 i49->h1 i50->h1 i51->h1 i52->h1 i53->h1
## -0.64 -1.08 -1.53 -1.94 -2.01 -1.78 -2.05 -2.44 -0.61
## i54->h1 i55->h1 i56->h1 i57->h1 i58->h1 i59->h1 i60->h1 i61->h1 i62->h1
## 0.13 0.32 0.46 -0.49 -0.17 0.16 1.18 1.04 0.77
## i63->h1 i64->h1 i65->h1 i66->h1 i67->h1 i68->h1 i69->h1 i70->h1 i71->h1
## 1.81 -0.84 0.00 0.00 0.00 -0.21 0.00 0.00 -0.94
## i72->h1 i73->h1 i74->h1 i75->h1 i76->h1 i77->h1 i78->h1 i79->h1 i80->h1
## 0.00 0.00 0.13 0.00 0.00 0.02 -0.18 0.00 0.03
## i81->h1
## 0.00
## b->o h1->o
## 3.78 -6.89
## sold.fctr sold.fctr.predict.RFE.X.Train.nnet.N
## 1 N 150
## 2 Y 39
## sold.fctr.predict.RFE.X.Train.nnet.Y
## 1 47
## 2 599
## Prediction
## Reference N Y
## N 150 47
## Y 39 599
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.970060e-01 7.102613e-01 8.743699e-01 9.167905e-01 7.640719e-01
## AccuracyPValue McnemarPValue
## 4.340700e-23 4.503513e-01
## id
## 1 RFE.X.Train.nnet
## feats
## 1 spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 23.377 0.133
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8240178 0.6903553 0.9576803 0.9178827
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.9330218 0.8387078
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8743699 0.9167905 0.5310511
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01695566 0.04066946## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: RFE.X.Train.avNNet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting size = 1, decay = 0.1, bag = FALSE on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: decay## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: bag
## Length Class Mode
## model 5 -none- list
## repeats 1 -none- numeric
## bag 1 -none- logical
## names 81 -none- character
## terms 3 terms call
## coefnames 81 -none- character
## xlevels 0 -none- list
## xNames 81 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 2 -none- character
## sold.fctr sold.fctr.predict.RFE.X.Train.avNNet.N
## 1 N 146
## 2 Y 20
## sold.fctr.predict.RFE.X.Train.avNNet.Y
## 1 51
## 2 618
## Prediction
## Reference N Y
## N 146 51
## Y 20 618
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.149701e-01 7.505900e-01 8.939545e-01 9.329956e-01 7.640719e-01
## AccuracyPValue McnemarPValue
## 3.277528e-30 3.703679e-04
## id
## 1 RFE.X.Train.avNNet
## feats
## 1 spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 64.106 0.205
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8548844 0.7411168 0.968652 0.9048502
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9456771 0.849078
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8939545 0.9329956 0.5592695
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01754865 0.05043553## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: RFE.X.Train.earth"
## [1] " indep_vars: spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting nprune = 14, degree = 1 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: degree
## Call: earth(x=matrix[835,81], y=factor.object, keepxy=TRUE,
## glm=list(family=function.object), degree=1, nprune=14)
##
## GLM coefficients
## Y
## (Intercept) -0.7295336
## cellular.fctrUnknown -0.8242766
## prdl.descr.my.fctriPadAir#0 1.6230875
## prdl.descr.my.fctriPadAir2#0 1.2801031
## spdiff.cut.fctr(-100,-10] 1.9700852
## spdiff.cut.fctr(-10,-1] 3.1723210
## spdiff.cut.fctr(-1,0] 19.4150395
## spdiff.cut.fctr(0,1] 4.8312385
## spdiff.cut.fctr(1,10] 3.6585767
## spdiff.cut.fctr(10,100] 3.5170490
## spdiff.cut.fctr(100,1e+03] 3.8004422
## h(sprice.log10-4.12713) -0.7034137
##
## Earth selected 12 of 65 terms, and 11 of 81 predictors
## Termination condition: RSq changed by less than 0.001 at 65 terms
## Importance: spdiff.cut.fctr(10,100], sprice.log10, ...
## Number of terms at each degree of interaction: 1 11 (additive model)
## Earth GCV 0.1073076 RSS 84.73364 GRSq 0.4061517 RSq 0.4370686
##
## GLM null.deviance 912.3891 (834 dof) deviance 540.677 (823 dof) iters 17
## sold.fctr sold.fctr.predict.RFE.X.Train.earth.N
## 1 N 106
## 2 Y 24
## sold.fctr.predict.RFE.X.Train.earth.Y
## 1 91
## 2 614
## Prediction
## Reference N Y
## N 106 91
## Y 24 614
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.622754e-01 5.671137e-01 8.370230e-01 8.849368e-01 7.640719e-01
## AccuracyPValue McnemarPValue
## 9.731418e-13 7.529965e-10
## id
## 1 RFE.X.Train.earth
## feats
## 1 spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 7.024 0.826
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7572442 0.5583756 0.9561129 0.8986999
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9143708 0.8450754
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.837023 0.8849368 0.5204435
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02008335 0.06548682## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.6## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.6
## [1] "fitting model: RFE.X.Train.rpart"
## [1] " indep_vars: spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr"
## + Fold1.Rep1: cp=0.02538
## - Fold1.Rep1: cp=0.02538
## + Fold2.Rep1: cp=0.02538
## - Fold2.Rep1: cp=0.02538
## + Fold3.Rep1: cp=0.02538
## - Fold3.Rep1: cp=0.02538
## + Fold1.Rep2: cp=0.02538
## - Fold1.Rep2: cp=0.02538
## + Fold2.Rep2: cp=0.02538
## - Fold2.Rep2: cp=0.02538
## + Fold3.Rep2: cp=0.02538
## - Fold3.Rep2: cp=0.02538
## + Fold1.Rep3: cp=0.02538
## - Fold1.Rep3: cp=0.02538
## + Fold2.Rep3: cp=0.02538
## - Fold2.Rep3: cp=0.02538
## + Fold3.Rep3: cp=0.02538
## - Fold3.Rep3: cp=0.02538
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0305 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 0.10406091 0 1.0000000
## 2 0.08121827 2 0.7918782
## 3 0.05583756 3 0.7106599
## 4 0.03045685 4 0.6548223
##
## Variable importance
## sprice.d20nexp sprice.log10
## 26 26
## sprice.root2 spdiff.cut.fctr(10,100]
## 26 8
## spdiff.cut.fctr(100,1e+03] condition.fctrNew
## 4 4
## spdiff.cut.fctr(-100,-10] prdl.descr.my.fctriPadAir2#0
## 3 2
## color.fctrSpace Gray
## 1
##
## Node number 1: 835 observations, complexity param=0.1040609
## predicted class=Y expected loss=0.2359281 P(node) =1
## class counts: 197 638
## probabilities: 0.236 0.764
## left son=2 (293 obs) right son=3 (542 obs)
## Primary splits:
## sprice.d20nexp < 0.00091211 to the left, improve=63.771110, (0 missing)
## sprice.log10 < 4.941607 to the right, improve=63.771110, (0 missing)
## sprice.root2 < 11.83195 to the right, improve=63.771110, (0 missing)
## spdiff.cut.fctr(-1,0] < 0.5 to the left, improve=14.847090, (0 missing)
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve= 8.103646, (0 missing)
## Surrogate splits:
## sprice.log10 < 4.941607 to the right, agree=1.000, adj=1.000, (0 split)
## sprice.root2 < 11.83195 to the right, agree=1.000, adj=1.000, (0 split)
## condition.fctrNew < 0.5 to the right, agree=0.698, adj=0.140, (0 split)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the right, agree=0.683, adj=0.096, (0 split)
## color.fctrSpace Gray < 0.5 to the right, agree=0.662, adj=0.038, (0 split)
##
## Node number 2: 293 observations, complexity param=0.1040609
## predicted class=N expected loss=0.4982935 P(node) =0.3508982
## class counts: 147 146
## probabilities: 0.502 0.498
## left son=4 (247 obs) right son=5 (46 obs)
## Primary splits:
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve=20.792430, (0 missing)
## sprice.d20nexp < 1.297877e-09 to the left, improve= 5.969295, (0 missing)
## sprice.log10 < 6.024113 to the right, improve= 5.969295, (0 missing)
## sprice.root2 < 20.33187 to the right, improve= 5.969295, (0 missing)
## spdiff.cut.fctr(100,1e+03] < 0.5 to the left, improve= 5.753613, (0 missing)
##
## Node number 3: 542 observations
## predicted class=Y expected loss=0.09225092 P(node) =0.6491018
## class counts: 50 492
## probabilities: 0.092 0.908
##
## Node number 4: 247 observations, complexity param=0.08121827
## predicted class=N expected loss=0.417004 P(node) =0.2958084
## class counts: 144 103
## probabilities: 0.583 0.417
## left son=8 (161 obs) right son=9 (86 obs)
## Primary splits:
## spdiff.cut.fctr(-100,-10] < 0.5 to the left, improve=8.175600, (0 missing)
## spdiff.cut.fctr(100,1e+03] < 0.5 to the left, improve=7.825980, (0 missing)
## sprice.log10 < 6.024113 to the right, improve=4.562231, (0 missing)
## sprice.root2 < 20.33187 to the right, improve=4.562231, (0 missing)
## sprice.d20nexp < 1.297877e-09 to the left, improve=4.562231, (0 missing)
## Surrogate splits:
## sprice.d20nexp < 0.0001586596 to the left, agree=0.696, adj=0.128, (0 split)
## sprice.log10 < 5.075143 to the right, agree=0.696, adj=0.128, (0 split)
## sprice.root2 < 12.64891 to the right, agree=0.696, adj=0.128, (0 split)
## prdl.descr.my.fctriPad2#0 < 0.5 to the left, agree=0.672, adj=0.058, (0 split)
## prdl.descr.my.fctriPadmini#0 < 0.5 to the left, agree=0.668, adj=0.047, (0 split)
##
## Node number 5: 46 observations
## predicted class=Y expected loss=0.06521739 P(node) =0.05508982
## class counts: 3 43
## probabilities: 0.065 0.935
##
## Node number 8: 161 observations, complexity param=0.05583756
## predicted class=N expected loss=0.3229814 P(node) =0.1928144
## class counts: 109 52
## probabilities: 0.677 0.323
## left son=16 (150 obs) right son=17 (11 obs)
## Primary splits:
## spdiff.cut.fctr(100,1e+03] < 0.5 to the left, improve=10.823270, (0 missing)
## spdiff.cut.fctr(-10,-1] < 0.5 to the left, improve= 4.728730, (0 missing)
## spdiff.cut.fctr(1,10] < 0.5 to the left, improve= 3.943564, (0 missing)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve= 3.823563, (0 missing)
## sprice.d20nexp < 3.431827e-05 to the left, improve= 3.746473, (0 missing)
##
## Node number 9: 86 observations
## predicted class=Y expected loss=0.4069767 P(node) =0.102994
## class counts: 35 51
## probabilities: 0.407 0.593
##
## Node number 16: 150 observations
## predicted class=N expected loss=0.2733333 P(node) =0.1796407
## class counts: 109 41
## probabilities: 0.727 0.273
##
## Node number 17: 11 observations
## predicted class=Y expected loss=0 P(node) =0.01317365
## class counts: 0 11
## probabilities: 0.000 1.000
##
## n= 835
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 835 197 Y (0.23592814 0.76407186)
## 2) sprice.d20nexp< 0.00091211 293 146 N (0.50170648 0.49829352)
## 4) spdiff.cut.fctr(10,100]< 0.5 247 103 N (0.58299595 0.41700405)
## 8) spdiff.cut.fctr(-100,-10]< 0.5 161 52 N (0.67701863 0.32298137)
## 16) spdiff.cut.fctr(100,1e+03]< 0.5 150 41 N (0.72666667 0.27333333) *
## 17) spdiff.cut.fctr(100,1e+03]>=0.5 11 0 Y (0.00000000 1.00000000) *
## 9) spdiff.cut.fctr(-100,-10]>=0.5 86 35 Y (0.40697674 0.59302326) *
## 5) spdiff.cut.fctr(10,100]>=0.5 46 3 Y (0.06521739 0.93478261) *
## 3) sprice.d20nexp>=0.00091211 542 50 Y (0.09225092 0.90774908) *
## sold.fctr sold.fctr.predict.RFE.X.Train.rpart.N
## 1 N 109
## 2 Y 41
## sold.fctr.predict.RFE.X.Train.rpart.Y
## 1 88
## 2 597
## Prediction
## Reference N Y
## N 109 88
## Y 41 597
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.455090e-01 5.329836e-01 8.191826e-01 8.693679e-01 7.640719e-01
## AccuracyPValue McnemarPValue
## 4.261678e-09 5.120140e-05
## id
## 1 RFE.X.Train.rpart
## feats
## 1 spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 2.824 0.058
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7445181 0.5532995 0.9357367 0.8147884
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9024943 0.8322942
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8191826 0.8693679 0.5042916
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02570214 0.06160521## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: RFE.X.Train.glmnet"
## [1] " indep_vars: spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00398 on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 8100 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 81 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 5.1862844308
## .rnorm
## -0.1107771720
## D.chrs.pnct11.n.log
## 0.0418445375
## D.chrs.pnct13.n.log
## -0.0410724986
## D.ratio.weight.sum.wrds.n
## -0.0710145839
## D.weight.sum.stem.stop.Ratio
## -3.1978462227
## D.wrds.stop.n.log
## 0.2132178382
## cellular.fctr1
## 0.0342741946
## cellular.fctrUnknown
## -0.2972590713
## color.fctrGold
## 0.0386216673
## color.fctrUnknown
## -0.2954790818
## color.fctrWhite
## -0.1763828281
## condition.fctrFor parts or not working
## -0.7976564204
## condition.fctrManufacturer refurbished
## -0.4533086862
## condition.fctrNew
## 0.0349740642
## condition.fctrSeller refurbished
## -0.8220906641
## prdl.descr.my.fctrUnknown#1
## 0.0096855790
## prdl.descr.my.fctriPad1#0
## -0.4667162086
## prdl.descr.my.fctriPad1#1
## -0.7420891530
## prdl.descr.my.fctriPad2#1
## -0.0003596699
## prdl.descr.my.fctriPad3#0
## 0.7076159994
## prdl.descr.my.fctriPad4#0
## 0.2859686278
## prdl.descr.my.fctriPad4#1
## -0.2114057089
## prdl.descr.my.fctriPadAir#0
## 1.6394504863
## prdl.descr.my.fctriPadAir#1
## 0.9952122197
## prdl.descr.my.fctriPadAir2#0
## 1.8499524257
## prdl.descr.my.fctriPadAir2#1
## 1.9496769623
## prdl.descr.my.fctriPadmini#0
## -0.0774319904
## prdl.descr.my.fctriPadmini#1
## -0.3986964142
## prdl.descr.my.fctriPadmini2#0
## 0.2009683282
## prdl.descr.my.fctriPadmini2#1
## 0.1303830664
## prdl.descr.my.fctriPadmini3#0
## 0.9008209036
## prdl.descr.my.fctriPadmini3#1
## 0.1871172763
## spdiff.cut.fctr(-100,-10]
## 1.3320806853
## spdiff.cut.fctr(-10,-1]
## 2.3094644370
## spdiff.cut.fctr(-1,0]
## 3.6626026661
## spdiff.cut.fctr(0,1]
## 2.6890377081
## spdiff.cut.fctr(1,10]
## 2.5343672892
## spdiff.cut.fctr(10,100]
## 2.4567967954
## spdiff.cut.fctr(100,1e+03]
## 2.4719006570
## sprice.root2
## -0.2050501420
## startprice.dcm1.is9
## -0.2141684266
## startprice.dgt2.is9
## 0.0844988048
## storage.fctr16
## -0.1339942870
## storage.fctrUnknown
## -0.0898886335
## cellular.fctr1:carrier.fctrSprint
## 0.6166161838
## cellular.fctr1:carrier.fctrUnknown
## 0.1643995614
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.2482053278
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 5.35861030
## .rnorm
## -0.11556025
## D.chrs.pnct11.n.log
## 0.04684166
## D.chrs.pnct13.n.log
## -0.05694223
## D.ratio.weight.sum.wrds.n
## -0.07871712
## D.weight.sum.stem.stop.Ratio
## -3.37453234
## D.wrds.stop.n.log
## 0.22639215
## cellular.fctr1
## 0.03251170
## cellular.fctrUnknown
## -0.30342577
## color.fctrGold
## 0.05916756
## color.fctrUnknown
## -0.30265531
## color.fctrWhite
## -0.19434392
## condition.fctrFor parts or not working
## -0.82116375
## condition.fctrManufacturer refurbished
## -0.46491320
## condition.fctrNew
## 0.04888803
## condition.fctrSeller refurbished
## -0.84441784
## prdl.descr.my.fctrUnknown#1
## 0.04129547
## prdl.descr.my.fctriPad1#0
## -0.49501420
## prdl.descr.my.fctriPad1#1
## -0.76488448
## prdl.descr.my.fctriPad2#1
## -0.01163942
## prdl.descr.my.fctriPad3#0
## 0.73864803
## prdl.descr.my.fctriPad4#0
## 0.32104712
## prdl.descr.my.fctriPad4#1
## -0.20552170
## prdl.descr.my.fctriPadAir#0
## 1.68998600
## prdl.descr.my.fctriPadAir#1
## 1.04448075
## prdl.descr.my.fctriPadAir2#0
## 1.88881459
## prdl.descr.my.fctriPadAir2#1
## 2.02373665
## prdl.descr.my.fctriPadmini#0
## -0.07546611
## prdl.descr.my.fctriPadmini#1
## -0.40885442
## prdl.descr.my.fctriPadmini2#0
## 0.22204240
## prdl.descr.my.fctriPadmini2#1
## 0.16562158
## prdl.descr.my.fctriPadmini3#0
## 0.94417819
## prdl.descr.my.fctriPadmini3#1
## 0.22978180
## spdiff.cut.fctr(-100,-10]
## 1.38520046
## spdiff.cut.fctr(-10,-1]
## 2.37323343
## spdiff.cut.fctr(-1,0]
## 3.79325486
## spdiff.cut.fctr(0,1]
## 2.77453129
## spdiff.cut.fctr(1,10]
## 2.60000109
## spdiff.cut.fctr(10,100]
## 2.50263237
## spdiff.cut.fctr(100,1e+03]
## 2.51558399
## sprice.root2
## -0.20771740
## startprice.dcm1.is9
## -0.22381228
## startprice.dgt2.is9
## 0.09570419
## storage.fctr16
## -0.14948970
## storage.fctrUnknown
## -0.10490394
## cellular.fctr1:carrier.fctrOther
## 0.04918983
## cellular.fctr1:carrier.fctrSprint
## 0.68765863
## cellular.fctr1:carrier.fctrUnknown
## 0.18065387
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.24919134
## sold.fctr sold.fctr.predict.RFE.X.Train.glmnet.N
## 1 N 118
## 2 Y 25
## sold.fctr.predict.RFE.X.Train.glmnet.Y
## 1 79
## 2 613
## Prediction
## Reference N Y
## N 118 79
## Y 25 613
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.754491e-01 6.183830e-01 8.511207e-01 8.970885e-01 7.640719e-01
## AccuracyPValue McnemarPValue
## 3.456509e-16 2.024459e-07
## id
## 1 RFE.X.Train.glmnet
## feats
## 1 spdiff.cut.fctr,sprice.log10,sprice.root2,sprice.d20nexp,prdl.descr.my.fctr,startprice.dgt1.is9,condition.fctr,cellular.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.wrds.n.log,D.chrs.n.log,startprice.dcm1.is9,startprice.dgt2.is9,color.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum,storage.fctr,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,D.wrds.stop.n.log,.rnorm,D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 9.989 1.463
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7798999 0.5989848 0.960815 0.9146206
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9218045 0.847889
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8511207 0.8970885 0.5401539
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01442857 0.04245021## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5
## [1] "***************"
## [1] "Outliers in obsTrnDF not deleted yet"
## [1] "***************"
## [1] "fitting model: Final.Ensemble.glm"
## [1] " indep_vars: sold.fctr.predict.RFE.X.Train.gbm.prob,sold.fctr.predict.RFE.X.Train.glm.prob,sold.fctr.predict.RFE.X.Train.nnet.prob,sold.fctr.predict.RFE.X.Train.avNNet.prob,sold.fctr.predict.RFE.X.Train.earth.prob,sold.fctr.predict.RFE.X.Train.rpart.prob,sold.fctr.predict.RFE.X.Train.glmnet.prob"
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0307 0.1222 0.2022 0.2569 3.0666
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -3.6292 0.4673 -7.767
## sold.fctr.predict.RFE.X.Train.avNNet.prob 2.4942 1.7470 1.428
## sold.fctr.predict.RFE.X.Train.earth.prob 2.5011 1.6599 1.507
## sold.fctr.predict.RFE.X.Train.gbm.prob 13.3185 1.4657 9.087
## sold.fctr.predict.RFE.X.Train.glm.prob 7.6333 2.4748 3.084
## sold.fctr.predict.RFE.X.Train.glmnet.prob -15.9258 3.7693 -4.225
## sold.fctr.predict.RFE.X.Train.nnet.prob 0.2309 2.5871 0.089
## sold.fctr.predict.RFE.X.Train.rpart.prob -2.7985 0.9996 -2.800
## Pr(>|z|)
## (Intercept) 8.05e-15 ***
## sold.fctr.predict.RFE.X.Train.avNNet.prob 0.15338
## sold.fctr.predict.RFE.X.Train.earth.prob 0.13188
## sold.fctr.predict.RFE.X.Train.gbm.prob < 2e-16 ***
## sold.fctr.predict.RFE.X.Train.glm.prob 0.00204 **
## sold.fctr.predict.RFE.X.Train.glmnet.prob 2.39e-05 ***
## sold.fctr.predict.RFE.X.Train.nnet.prob 0.92888
## sold.fctr.predict.RFE.X.Train.rpart.prob 0.00511 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 911.31 on 832 degrees of freedom
## Residual deviance: 333.08 on 825 degrees of freedom
## AIC: 349.08
##
## Number of Fisher Scoring iterations: 6
## sold.fctr sold.fctr.predict.Final.Ensemble.glm.N
## 1 N 148
## 2 Y 12
## sold.fctr.predict.Final.Ensemble.glm.Y
## 1 49
## 2 624
## Prediction
## Reference N Y
## N 148 49
## Y 12 624
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.267707e-01 7.831664e-01 9.069248e-01 9.435256e-01 7.635054e-01
## AccuracyPValue McnemarPValue
## 8.642632e-36 4.039732e-06
## id
## 1 Final.Ensemble.glm
## feats
## 1 sold.fctr.predict.RFE.X.Train.gbm.prob,sold.fctr.predict.RFE.X.Train.glm.prob,sold.fctr.predict.RFE.X.Train.nnet.prob,sold.fctr.predict.RFE.X.Train.avNNet.prob,sold.fctr.predict.RFE.X.Train.earth.prob,sold.fctr.predict.RFE.X.Train.rpart.prob,sold.fctr.predict.RFE.X.Train.glmnet.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.176 0.011
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8854715 0.8071066 0.9638365 0.9586486
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.9533995 0.9219657
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9069248 0.9435256 0.7777387
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01803986 0.05448681
## [1] "fitting model: Final.Ensemble.glmnet"
## [1] " indep_vars: sold.fctr.predict.RFE.X.Train.gbm.prob,sold.fctr.predict.RFE.X.Train.glm.prob,sold.fctr.predict.RFE.X.Train.nnet.prob,sold.fctr.predict.RFE.X.Train.avNNet.prob,sold.fctr.predict.RFE.X.Train.earth.prob,sold.fctr.predict.RFE.X.Train.rpart.prob,sold.fctr.predict.RFE.X.Train.glmnet.prob"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00145 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = ifelse(grepl("Ensemble", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 86 -none- numeric
## beta 602 dgCMatrix S4
## df 86 -none- numeric
## dim 2 -none- numeric
## lambda 86 -none- numeric
## dev.ratio 86 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 7 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -4.021766
## sold.fctr.predict.RFE.X.Train.avNNet.prob
## 1.933228
## sold.fctr.predict.RFE.X.Train.gbm.prob
## 11.401741
## sold.fctr.predict.RFE.X.Train.glm.prob
## 3.369966
## sold.fctr.predict.RFE.X.Train.glmnet.prob
## -6.995942
## sold.fctr.predict.RFE.X.Train.rpart.prob
## -1.820202
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.998314
## sold.fctr.predict.RFE.X.Train.avNNet.prob
## 1.971678
## sold.fctr.predict.RFE.X.Train.gbm.prob
## 11.505428
## sold.fctr.predict.RFE.X.Train.glm.prob
## 3.662303
## sold.fctr.predict.RFE.X.Train.glmnet.prob
## -7.438555
## sold.fctr.predict.RFE.X.Train.rpart.prob
## -1.838924
## sold.fctr sold.fctr.predict.Final.Ensemble.glmnet.N
## 1 N 153
## 2 Y 18
## sold.fctr.predict.Final.Ensemble.glmnet.Y
## 1 44
## 2 618
## Prediction
## Reference N Y
## N 153 44
## Y 18 618
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.255702e-01 7.840615e-01 9.055970e-01 9.424621e-01 7.635054e-01
## AccuracyPValue McnemarPValue
## 3.353432e-35 1.498349e-03
## id
## 1 Final.Ensemble.glmnet
## feats
## 1 sold.fctr.predict.RFE.X.Train.gbm.prob,sold.fctr.predict.RFE.X.Train.glm.prob,sold.fctr.predict.RFE.X.Train.nnet.prob,sold.fctr.predict.RFE.X.Train.avNNet.prob,sold.fctr.predict.RFE.X.Train.earth.prob,sold.fctr.predict.RFE.X.Train.rpart.prob,sold.fctr.predict.RFE.X.Train.glmnet.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.459 0.051
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8796092 0.7969543 0.9622642 0.9600374
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9522342 0.9223682
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.905597 0.9424621 0.7776879
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02000683 0.0601057rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 14 fit.data.training 7 0 0 541.552 721.652
## 15 fit.data.training 7 1 1 721.652 NA
## elapsed
## 14 180.1
## 15 NA#stop(here"); glb_to_sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glb_fin_mdl_id)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", glb_rsp_var_out, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
glb_newobs_df <- glb_get_predictions(df=glb_newobs_df, mdl_id=mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
}
}## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.2glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## Ensemble.glmnet.importance
## sold.fctr.predict.RFE.X.Train.gbm.prob NA
## sold.fctr.predict.RFE.X.Train.glm.prob NA
## sold.fctr.predict.RFE.X.Train.avNNet.prob NA
## sold.fctr.predict.RFE.X.Train.earth.prob NA
## sold.fctr.predict.RFE.X.Train.nnet.prob NA
## sold.fctr.predict.RFE.X.Train.rpart.prob NA
## sold.fctr.predict.RFE.X.Train.glmnet.prob NA
## sold.fctr.predict.RFE.X.avNNet.prob 59.94612
## sold.fctr.predict.RFE.X.bayesglm.prob 0.00000
## sold.fctr.predict.RFE.X.earth.prob 45.08651
## sold.fctr.predict.RFE.X.gbm.prob 100.00000
## sold.fctr.predict.RFE.X.glm.prob 86.57999
## sold.fctr.predict.RFE.X.glmnet.prob 20.98238
## sold.fctr.predict.RFE.X.nnet.prob 61.05088
## sold.fctr.predict.RFE.X.rpart.prob 25.54264
## importance
## sold.fctr.predict.RFE.X.Train.gbm.prob 100.00000
## sold.fctr.predict.RFE.X.Train.glm.prob 57.62471
## sold.fctr.predict.RFE.X.Train.avNNet.prob 49.18183
## sold.fctr.predict.RFE.X.Train.earth.prob 38.73069
## sold.fctr.predict.RFE.X.Train.nnet.prob 38.73069
## sold.fctr.predict.RFE.X.Train.rpart.prob 28.94300
## sold.fctr.predict.RFE.X.Train.glmnet.prob 0.00000
## sold.fctr.predict.RFE.X.avNNet.prob NA
## sold.fctr.predict.RFE.X.bayesglm.prob NA
## sold.fctr.predict.RFE.X.earth.prob NA
## sold.fctr.predict.RFE.X.gbm.prob NA
## sold.fctr.predict.RFE.X.glm.prob NA
## sold.fctr.predict.RFE.X.glmnet.prob NA
## sold.fctr.predict.RFE.X.nnet.prob NA
## sold.fctr.predict.RFE.X.rpart.prob NA
## Final.Ensemble.glmnet.importance
## sold.fctr.predict.RFE.X.Train.gbm.prob 100.00000
## sold.fctr.predict.RFE.X.Train.glm.prob 57.62471
## sold.fctr.predict.RFE.X.Train.avNNet.prob 49.18183
## sold.fctr.predict.RFE.X.Train.earth.prob 38.73069
## sold.fctr.predict.RFE.X.Train.nnet.prob 38.73069
## sold.fctr.predict.RFE.X.Train.rpart.prob 28.94300
## sold.fctr.predict.RFE.X.Train.glmnet.prob 0.00000
## sold.fctr.predict.RFE.X.avNNet.prob NA
## sold.fctr.predict.RFE.X.bayesglm.prob NA
## sold.fctr.predict.RFE.X.earth.prob NA
## sold.fctr.predict.RFE.X.gbm.prob NA
## sold.fctr.predict.RFE.X.glm.prob NA
## sold.fctr.predict.RFE.X.glmnet.prob NA
## sold.fctr.predict.RFE.X.nnet.prob NA
## sold.fctr.predict.RFE.X.rpart.prob NAif (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : No features in selected model are statistically importantdsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.RFE.X.Train.gbm.prob"
## [2] "sold.fctr.predict.RFE.X.Train.gbm"
## [3] "sold.fctr.predict.RFE.X.Train.gbm.err"
## [4] "sold.fctr.predict.RFE.X.Train.gbm.err.abs"
## [5] "sold.fctr.predict.RFE.X.Train.gbm.accurate"
## [6] "sold.fctr.predict.RFE.X.Train.glm.prob"
## [7] "sold.fctr.predict.RFE.X.Train.glm"
## [8] "sold.fctr.predict.RFE.X.Train.glm.err"
## [9] "sold.fctr.predict.RFE.X.Train.glm.err.abs"
## [10] "sold.fctr.predict.RFE.X.Train.glm.accurate"
## [11] "sold.fctr.predict.RFE.X.Train.nnet.prob"
## [12] "sold.fctr.predict.RFE.X.Train.nnet"
## [13] "sold.fctr.predict.RFE.X.Train.nnet.err"
## [14] "sold.fctr.predict.RFE.X.Train.nnet.err.abs"
## [15] "sold.fctr.predict.RFE.X.Train.nnet.accurate"
## [16] "sold.fctr.predict.RFE.X.Train.avNNet.prob"
## [17] "sold.fctr.predict.RFE.X.Train.avNNet"
## [18] "sold.fctr.predict.RFE.X.Train.avNNet.err"
## [19] "sold.fctr.predict.RFE.X.Train.avNNet.err.abs"
## [20] "sold.fctr.predict.RFE.X.Train.avNNet.accurate"
## [21] "sold.fctr.predict.RFE.X.Train.earth.prob"
## [22] "sold.fctr.predict.RFE.X.Train.earth"
## [23] "sold.fctr.predict.RFE.X.Train.earth.err"
## [24] "sold.fctr.predict.RFE.X.Train.earth.err.abs"
## [25] "sold.fctr.predict.RFE.X.Train.earth.accurate"
## [26] "sold.fctr.predict.RFE.X.Train.rpart.prob"
## [27] "sold.fctr.predict.RFE.X.Train.rpart"
## [28] "sold.fctr.predict.RFE.X.Train.rpart.err"
## [29] "sold.fctr.predict.RFE.X.Train.rpart.err.abs"
## [30] "sold.fctr.predict.RFE.X.Train.rpart.accurate"
## [31] "sold.fctr.predict.RFE.X.Train.glmnet.prob"
## [32] "sold.fctr.predict.RFE.X.Train.glmnet"
## [33] "sold.fctr.predict.RFE.X.Train.glmnet.err"
## [34] "sold.fctr.predict.RFE.X.Train.glmnet.err.abs"
## [35] "sold.fctr.predict.RFE.X.Train.glmnet.accurate"
## [36] "sold.fctr.predict.Final.Ensemble.glmnet.prob"
## [37] "sold.fctr.predict.Final.Ensemble.glmnet"
## [38] "sold.fctr.predict.Final.Ensemble.glmnet.err"
## [39] "sold.fctr.predict.Final.Ensemble.glmnet.err.abs"
## [40] "sold.fctr.predict.Final.Ensemble.glmnet.accurate"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 15 fit.data.training 7 1 1 721.652 731.345
## 16 predict.data.new 8 0 0 731.346 NA
## elapsed
## 15 9.693
## 16 NAStep 8.0: predict data new
# Compute final model predictions
#glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); all.equal(sav_trnobs_df, glb_trnobs_df); all.equal(sav_newobs_df, glb_newobs_df)
if (glb_is_classification && glb_is_binomial)
prob_threshold_def <-
glb_models_df[glb_models_df$id == glb_sel_mdl_id, "opt.prob.threshold.OOB"] else
prob_threshold_def <- NULL
for (obsSet in c("trn", "new")) {
obs_df <- switch(obsSet, all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
obs_df <- glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id,
rsp_var_out = glb_rsp_var_out, prob_threshold_def = prob_threshold_def)
if (obsSet == "all") glb_allobs_df <- obs_df else
if (obsSet == "trn") glb_trnobs_df <- obs_df else
if (obsSet == "new") glb_newobs_df <- obs_df
}## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var_out
## = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var_out
## = glb_rsp_var_out, : Using default probability threshold: 0.2rm(obs_df)
glb_allobs_df <- orderBy(reformulate(glb_id_var), myrbind_df(glb_trnobs_df, glb_newobs_df))
glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id = glb_fin_mdl_id,
prob_threshold = prob_threshold_def)## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : No features in selected model are statistically importantif (is.null(glb_out_obs)) obs_df <- glb_newobs_df else
obs_df <- switch(glb_out_obs,
all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
require(stringr)
if (glb_is_classification && glb_is_binomial) {
# submit_df <- glb_newobs_df[, c(glb_id_var,
# paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
# names(submit_df)[2] <- "Probability1"
obsout_df <- obs_df[, glb_id_var, FALSE]
for (clmn in names(glb_out_vars_lst))
if (!grepl("^%<d-%", glb_out_vars_lst[[clmn]]))
obsout_df[, clmn] <- obs_df[, glb_out_vars_lst[[clmn]]] else {
feat <- str_trim(unlist(strsplit(glb_out_vars_lst[[clmn]], "%<d-%"))[2])
obsout_df[, clmn] <- obs_df[, eval(parse(text = feat))]
}
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11932, 11943,
12050, 12115, 12171,
12253, 12285, 12367, 12388, 12399,
12585)
for (obs_id in glb_force_prediction_lst[["0"]]) {
if (sum(glb_allobs_df[, glb_id_var] == obs_id) == 0)
next
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11982, 11990, 11991, 11994, 11999,
12000, 12002, 12004, 12018, 12021, 12065, 12072,
12111, 12114, 12126, 12134, 12152, 12172,
12213, 12214, 12233, 12265, 12278, 12299,
12446, 12491,
12505, 12576, 12608, 12630)
for (obs_id in glb_force_prediction_lst[["1"]]) {
if (sum(glb_allobs_df[, glb_id_var] == obs_id) == 0)
next
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
}
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
for (obs_id in glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y") &
(glb_newobs_df[ , "startprice"] > 675), "UniqueID"]) {
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
} else {
# submit_df <- glb_newobs_df[, c(glb_id_var,
# paste0(glb_rsp_var_out, glb_fin_mdl_id))]
obsout_df <- obs_df[, glb_id_var, FALSE]
for (clmn in names(glb_out_vars_lst))
if (!grepl("^%<d-%", glb_out_vars_lst[[clmn]]))
obsout_df[, clmn] <- obs_df[, glb_out_vars_lst[[clmn]]] else {
feat <- str_trim(unlist(strsplit(glb_out_vars_lst[[clmn]], "%<d-%"))[2])
obsout_df[, clmn] <- obs_df[, eval(parse(text=feat))]
}
}
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by = ".grpid", all.x = TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, obsout_df, by = glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 > 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 < 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print(sprintf("Prediction errors in duplicates: %d", nrow(tmp_newobs_df)))
print(tmp_newobs_df)
# if (nrow(tmp_newobs_df) > 0)
# stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
tmp_newobs_df <- cbind(glb_newobs_df, obsout_df[, "Probability1", FALSE])
# if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
# (tmp_newobs_df[, "Probability1"] >= 0.5), "startprice"]) >
# max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
# (glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
# stop("startprice for some +ve predictions > 675")
# Check predictions that are outside of data ranges
#stop(here")
require(stringr)
tmp_feats_df <- subset(glb_feats_df,
!nzv &
(exclude.as.feat != 1) &
!grepl(".fctr", id, fixed=TRUE))[, "id", FALSE]
ranges_all_df <- glb_allobs_df[, tmp_feats_df$id] %>%
dplyr::summarise_each(funs(min(., na.rm=TRUE),
max(., na.rm=TRUE))) %>%
tidyr::gather() %>%
dplyr::mutate(id=str_sub(key, 1, -5),
stat=str_sub(key, -3)) %>%
dplyr::select(-key) %>%
tidyr::spread(stat, value)
# sav_ranges_trn_df <- ranges_trn_df; all.equal(sav_ranges_trn_df, ranges_trn_df)
# sav_ranges_new_df <- ranges_new_df; all.equal(sav_ranges_new_df, ranges_new_df)
get_ranges_df <- function(obs_df, feats, class_var) {
require(tidyr)
ranges_df <- obs_df[, c(class_var, feats)] %>%
dplyr::group_by_(class_var) %>%
dplyr::summarise_each(funs(min(., na.rm=TRUE),
max(., na.rm=TRUE))) %>%
tidyr::gather(key, value, -1) %>%
mutate(id=str_sub(key, 1, -5),
stat.vname=paste0(str_sub(key, -3), ".", class_var)) %>%
unite_("stat.class", c("stat.vname", class_var), sep=".") %>%
dplyr::select(-key) %>%
spread(stat.class, value)
return(ranges_df)
}
rsp_var_out_OOB <- paste0(glb_rsp_var_out, glb_sel_mdl_id)
rsp_var_out_new <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
ranges_trn_df <- get_ranges_df(obs_df=glb_trnobs_df, feats=tmp_feats_df$id,
class_var=glb_rsp_var)
ranges_fit_df <- get_ranges_df(obs_df=glb_fitobs_df, feats=tmp_feats_df$id,
class_var=glb_rsp_var)
ranges_OOB_df <- get_ranges_df(obs_df=glb_OOBobs_df, feats=tmp_feats_df$id,
class_var=rsp_var_out_OOB)
ranges_new_df <- get_ranges_df(obs_df=glb_newobs_df, feats=tmp_feats_df$id,
class_var=rsp_var_out_new)
for (obsset in c("OOB", "new")) {
if (obsset == "OOB") {
ranges_ref_df <- ranges_fit_df; obs_df <- glb_OOBobs_df;
rsp_var_out_obs <- rsp_var_out_OOB; sprintf_pfx <- "OOBobs";
} else {
ranges_ref_df <- ranges_trn_df; obs_df <- glb_newobs_df;
rsp_var_out_obs <- rsp_var_out_new; sprintf_pfx <- "newobs";
}
plt_feats_df <- glb_feats_df %>%
merge(ranges_all_df, all=TRUE) %>%
merge(ranges_ref_df, all=TRUE) %>%
merge(ranges_OOB_df, all=TRUE) %>%
merge(ranges_new_df, all=TRUE) %>%
subset(!is.na(min) & (id != ".rnorm"))
row.names(plt_feats_df) <- plt_feats_df$id
range_outlier_ids <- c(NULL)
for (clss in unique(obs_df[, rsp_var_out_obs])) {
for (stat in c("min", "max")) {
if (stat == "min") {
dsp_feats <- plt_feats_df[
which(plt_feats_df[, paste("min", rsp_var_out_obs, clss, sep=".")] <
plt_feats_df[, paste("min", glb_rsp_var, clss, sep=".")]), "id"]
} else {
dsp_feats <- plt_feats_df[
which(plt_feats_df[, paste("max", rsp_var_out_obs, clss, sep=".")] >
plt_feats_df[, paste("max", glb_rsp_var, clss, sep=".")]), "id"]
}
if (length(dsp_feats) > 0) {
ths_ids <- c(NULL)
for (feat in dsp_feats) {
if (stat == "min") {
ths_ids <- union(ths_ids,
obs_df[(obs_df[, rsp_var_out_obs] == clss) &
(obs_df[, feat] <
plt_feats_df[plt_feats_df$id == feat, paste("min", glb_rsp_var, clss, sep=".")]),
glb_id_var])
} else {
ths_ids <- union(ths_ids,
obs_df[(obs_df[, rsp_var_out_obs] == clss) &
(obs_df[, feat] >
plt_feats_df[plt_feats_df$id == feat, paste("max", glb_rsp_var, clss, sep=".")]),
glb_id_var])
}
}
tmp_obs_df <- obs_df[obs_df[, glb_id_var] %in% ths_ids,
c(glb_id_var, rsp_var_out_obs, dsp_feats)]
if (stat == "min") {
print(sprintf("%s %s %s: min < min of Train range: %d",
sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
} else {
print(sprintf("%s %s %s: max > max of Train range: %d",
sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
}
myprint_df(tmp_obs_df)
print(subset(plt_feats_df, id %in% dsp_feats))
range_outlier_ids <- union(range_outlier_ids, ths_ids)
}
}
}
print(sprintf("%s total range outliers: %d", sprintf_pfx, length(range_outlier_ids)))
}
} ## [1] "Prediction errors in duplicates: 1"
## UniqueID .grpid sold.fctr.predict.Final.Ensemble.glmnet sold.0 sold.1
## 34 12214 65 Y 0 1
## sold.NA .freq Probability1 .err
## 34 1 2 0.4062594 TRUE
## [1] "OOBobs sold.fctr.predict.Ensemble.glmnet Y: min < min of Train range: 4"
## UniqueID sold.fctr.predict.Ensemble.glmnet D.ratio.wrds.stop.n.wrds.n
## 79 10201 Y 0.25000000
## 612 11134 Y 0.04545455
## 523 10885 Y 0.14285714
## 93 10227 Y 0.30000000
## D.weight.sum.stem.stop.Ratio
## 79 0.8311253
## 612 0.9893800
## 523 0.8466833
## 93 0.8128488
## id cor.y
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n -0.01535889
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio -0.05172679
## exclude.as.feat cor.y.abs cor.high.X
## D.ratio.wrds.stop.n.wrds.n FALSE 0.01535889 <NA>
## D.weight.sum.stem.stop.Ratio FALSE 0.05172679 <NA>
## freqRatio percentUnique zeroVar nzv
## D.ratio.wrds.stop.n.wrds.n 18.46429 7.784431 FALSE FALSE
## D.weight.sum.stem.stop.Ratio 89.16667 32.215569 FALSE FALSE
## is.cor.y.abs.low interaction.feat
## D.ratio.wrds.stop.n.wrds.n TRUE <NA>
## D.weight.sum.stem.stop.Ratio FALSE <NA>
## shapiro.test.p.value rsp_var_raw id_var
## D.ratio.wrds.stop.n.wrds.n 9.913172e-37 FALSE NA
## D.weight.sum.stem.stop.Ratio 3.397141e-41 FALSE NA
## rsp_var max min max.sold.fctr.N
## D.ratio.wrds.stop.n.wrds.n NA 1 0.04545455 1
## D.weight.sum.stem.stop.Ratio NA 1 0.71178250 1
## max.sold.fctr.Y min.sold.fctr.N
## D.ratio.wrds.stop.n.wrds.n 1 0.05263158
## D.weight.sum.stem.stop.Ratio 1 0.88071019
## min.sold.fctr.Y
## D.ratio.wrds.stop.n.wrds.n 0.0500000
## D.weight.sum.stem.stop.Ratio 0.8487443
## max.sold.fctr.predict.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## max.sold.fctr.predict.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## min.sold.fctr.predict.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 0.05263158
## D.weight.sum.stem.stop.Ratio 0.71178250
## min.sold.fctr.predict.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 0.04545455
## D.weight.sum.stem.stop.Ratio 0.81284882
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 0.0500000
## D.weight.sum.stem.stop.Ratio 0.8048203
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 0.05263158
## D.weight.sum.stem.stop.Ratio 0.77042467
## [1] "OOBobs sold.fctr.predict.Ensemble.glmnet Y: max > max of Train range: 4"
## UniqueID sold.fctr.predict.Ensemble.glmnet D.chrs.pnct11.n.log
## 518 10876 Y 0.000000
## 834 11839 Y 1.791759
## 32 10119 Y 0.000000
## 485 10814 Y 0.000000
## D.weight.post.stem.sum D.weight.post.stop.sum D.weight.sum
## 518 7.758246 8.003901 7.758246
## 834 5.434882 6.365720 5.434882
## 32 7.681785 7.681785 7.681785
## 485 8.241983 8.657021 8.241983
## id cor.y exclude.as.feat
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log 0.02862542 FALSE
## D.weight.post.stem.sum D.weight.post.stem.sum 0.02180097 FALSE
## D.weight.post.stop.sum D.weight.post.stop.sum 0.02443863 FALSE
## D.weight.sum D.weight.sum 0.02180097 FALSE
## cor.y.abs cor.high.X freqRatio percentUnique
## D.chrs.pnct11.n.log 0.02862542 <NA> 8.925926 0.7185629
## D.weight.post.stem.sum 0.02180097 <NA> 86.166667 34.0119760
## D.weight.post.stop.sum 0.02443863 <NA> 86.166667 34.0119760
## D.weight.sum 0.02180097 <NA> 86.166667 34.0119760
## zeroVar nzv is.cor.y.abs.low interaction.feat
## D.chrs.pnct11.n.log FALSE FALSE TRUE <NA>
## D.weight.post.stem.sum FALSE FALSE TRUE <NA>
## D.weight.post.stop.sum FALSE FALSE TRUE <NA>
## D.weight.sum FALSE FALSE TRUE <NA>
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.chrs.pnct11.n.log 1.776146e-45 FALSE NA NA
## D.weight.post.stem.sum 1.253951e-36 FALSE NA NA
## D.weight.post.stop.sum 1.585178e-36 FALSE NA NA
## D.weight.sum 1.253951e-36 FALSE NA NA
## max min max.sold.fctr.N max.sold.fctr.Y
## D.chrs.pnct11.n.log 1.791759 0 1.098612 1.609438
## D.weight.post.stem.sum 8.241983 0 6.782552 7.013305
## D.weight.post.stop.sum 8.657021 0 6.782552 7.044117
## D.weight.sum 8.241983 0 6.782552 7.013305
## min.sold.fctr.N min.sold.fctr.Y
## D.chrs.pnct11.n.log 0 0
## D.weight.post.stem.sum 0 0
## D.weight.post.stop.sum 0 0
## D.weight.sum 0 0
## max.sold.fctr.predict.Ensemble.glmnet.N
## D.chrs.pnct11.n.log 1.386294
## D.weight.post.stem.sum 5.915924
## D.weight.post.stop.sum 7.249557
## D.weight.sum 5.915924
## max.sold.fctr.predict.Ensemble.glmnet.Y
## D.chrs.pnct11.n.log 1.791759
## D.weight.post.stem.sum 8.241983
## D.weight.post.stop.sum 8.657021
## D.weight.sum 8.241983
## min.sold.fctr.predict.Ensemble.glmnet.N
## D.chrs.pnct11.n.log 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## min.sold.fctr.predict.Ensemble.glmnet.Y
## D.chrs.pnct11.n.log 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.pnct11.n.log 1.098612
## D.weight.post.stem.sum 6.539113
## D.weight.post.stop.sum 6.621118
## D.weight.sum 6.539113
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.pnct11.n.log 1.609438
## D.weight.post.stem.sum 6.912913
## D.weight.post.stop.sum 6.991631
## D.weight.sum 6.912913
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.pnct11.n.log 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.pnct11.n.log 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## [1] "OOBobs sold.fctr.predict.Ensemble.glmnet N: min < min of Train range: 4"
## UniqueID sold.fctr.predict.Ensemble.glmnet
## 613 11138 N
## 751 11650 N
## 720 11603 N
## 486 10816 N
## D.weight.sum.stem.stop.Ratio sprice.log10 sprice.root2
## 613 0.8591038 5.55682806 16.0934769
## 751 0.8048657 4.52178858 9.5916630
## 720 0.7117825 -0.01005034 0.9949874
## 486 0.9869062 -0.01005034 0.9949874
## id cor.y
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio -0.05172679
## sprice.log10 sprice.log10 -0.37729587
## sprice.root2 sprice.root2 -0.46922279
## exclude.as.feat cor.y.abs cor.high.X
## D.weight.sum.stem.stop.Ratio FALSE 0.05172679 <NA>
## sprice.log10 FALSE 0.37729587 sprice.root2
## sprice.root2 FALSE 0.46922279 <NA>
## freqRatio percentUnique zeroVar nzv
## D.weight.sum.stem.stop.Ratio 89.166667 32.21557 FALSE FALSE
## sprice.log10 3.560976 24.67066 FALSE FALSE
## sprice.root2 3.560976 24.67066 FALSE FALSE
## is.cor.y.abs.low interaction.feat
## D.weight.sum.stem.stop.Ratio FALSE <NA>
## sprice.log10 FALSE <NA>
## sprice.root2 FALSE <NA>
## shapiro.test.p.value rsp_var_raw id_var
## D.weight.sum.stem.stop.Ratio 3.397141e-41 FALSE NA
## sprice.log10 7.580418e-30 FALSE NA
## sprice.root2 5.910306e-16 FALSE NA
## rsp_var max min max.sold.fctr.N
## D.weight.sum.stem.stop.Ratio NA 1.000000 0.7117825 1.000000
## sprice.log10 NA 6.779911 -4.6051702 6.684612
## sprice.root2 NA 29.664625 0.1000000 28.284271
## max.sold.fctr.Y min.sold.fctr.N
## D.weight.sum.stem.stop.Ratio 1.000000 0.8807102
## sprice.log10 6.476972 2.9957323
## sprice.root2 25.495098 4.4721360
## min.sold.fctr.Y
## D.weight.sum.stem.stop.Ratio 0.8487443
## sprice.log10 -4.6051702
## sprice.root2 0.1000000
## max.sold.fctr.predict.Ensemble.glmnet.N
## D.weight.sum.stem.stop.Ratio 1.000000
## sprice.log10 6.593031
## sprice.root2 27.018327
## max.sold.fctr.predict.Ensemble.glmnet.Y
## D.weight.sum.stem.stop.Ratio 1.00000
## sprice.log10 6.35437
## sprice.root2 23.97916
## min.sold.fctr.predict.Ensemble.glmnet.N
## D.weight.sum.stem.stop.Ratio 0.71178250
## sprice.log10 -0.01005034
## sprice.root2 0.99498744
## min.sold.fctr.predict.Ensemble.glmnet.Y
## D.weight.sum.stem.stop.Ratio 0.8128488
## sprice.log10 -4.6051702
## sprice.root2 0.1000000
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.weight.sum.stem.stop.Ratio 1.000000
## sprice.log10 6.593031
## sprice.root2 27.018327
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.weight.sum.stem.stop.Ratio 1.000000
## sprice.log10 6.779911
## sprice.root2 29.664625
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.weight.sum.stem.stop.Ratio 0.8048203
## sprice.log10 4.3694479
## sprice.root2 8.8881944
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.weight.sum.stem.stop.Ratio 0.7704247
## sprice.log10 -4.6051702
## sprice.root2 0.1000000
## [1] "OOBobs sold.fctr.predict.Ensemble.glmnet N: max > max of Train range: 4"
## UniqueID sold.fctr.predict.Ensemble.glmnet D.chrs.pnct11.n.log
## 516 10869 N 1.386294
## 569 10997 N 0.000000
## 720 11603 N 0.000000
## 486 10816 N 0.000000
## D.ratio.weight.sum.wrds.n D.weight.post.stop.sum sprice.d20nexp
## 516 0.2959578 4.912897 3.059023e-07
## 569 4.7184212 4.750130 7.083409e-03
## 720 1.2900269 7.249557 9.517052e-01
## 486 1.0678988 4.328269 9.517052e-01
## id cor.y
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log 0.0286254240
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -0.0007877674
## D.weight.post.stop.sum D.weight.post.stop.sum 0.0244386322
## sprice.d20nexp sprice.d20nexp 0.3243319036
## exclude.as.feat cor.y.abs cor.high.X
## D.chrs.pnct11.n.log FALSE 0.0286254240 <NA>
## D.ratio.weight.sum.wrds.n FALSE 0.0007877674 <NA>
## D.weight.post.stop.sum FALSE 0.0244386322 <NA>
## sprice.d20nexp FALSE 0.3243319036 sprice.log10
## freqRatio percentUnique zeroVar nzv
## D.chrs.pnct11.n.log 8.925926 0.7185629 FALSE FALSE
## D.ratio.weight.sum.wrds.n 86.166667 34.0119760 FALSE FALSE
## D.weight.post.stop.sum 86.166667 34.0119760 FALSE FALSE
## sprice.d20nexp 3.560976 24.6706587 FALSE FALSE
## is.cor.y.abs.low interaction.feat
## D.chrs.pnct11.n.log TRUE <NA>
## D.ratio.weight.sum.wrds.n TRUE <NA>
## D.weight.post.stop.sum TRUE <NA>
## sprice.d20nexp FALSE <NA>
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.chrs.pnct11.n.log 1.776146e-45 FALSE NA NA
## D.ratio.weight.sum.wrds.n 2.472391e-44 FALSE NA NA
## D.weight.post.stop.sum 1.585178e-36 FALSE NA NA
## sprice.d20nexp 1.092982e-38 FALSE NA NA
## max min max.sold.fctr.N
## D.chrs.pnct11.n.log 1.7917595 0.000000e+00 1.0986123
## D.ratio.weight.sum.wrds.n 5.6570206 0.000000e+00 3.3912758
## D.weight.post.stop.sum 8.6570206 0.000000e+00 6.7825515
## sprice.d20nexp 0.9995001 7.785024e-20 0.3678794
## max.sold.fctr.Y min.sold.fctr.N min.sold.fctr.Y
## D.chrs.pnct11.n.log 1.6094379 0.000000e+00 0.000000e+00
## D.ratio.weight.sum.wrds.n 5.6570206 0.000000e+00 0.000000e+00
## D.weight.post.stop.sum 7.0441171 0.000000e+00 0.000000e+00
## sprice.d20nexp 0.9995001 4.248354e-18 7.681205e-15
## max.sold.fctr.predict.Ensemble.glmnet.N
## D.chrs.pnct11.n.log 1.3862944
## D.ratio.weight.sum.wrds.n 4.7184212
## D.weight.post.stop.sum 7.2495565
## sprice.d20nexp 0.9517052
## max.sold.fctr.predict.Ensemble.glmnet.Y
## D.chrs.pnct11.n.log 1.7917595
## D.ratio.weight.sum.wrds.n 5.6570206
## D.weight.post.stop.sum 8.6570206
## sprice.d20nexp 0.9995001
## min.sold.fctr.predict.Ensemble.glmnet.N
## D.chrs.pnct11.n.log 0.000000e+00
## D.ratio.weight.sum.wrds.n 0.000000e+00
## D.weight.post.stop.sum 0.000000e+00
## sprice.d20nexp 1.407565e-16
## min.sold.fctr.predict.Ensemble.glmnet.Y
## D.chrs.pnct11.n.log 0.000000e+00
## D.ratio.weight.sum.wrds.n 0.000000e+00
## D.weight.post.stop.sum 0.000000e+00
## sprice.d20nexp 3.266131e-13
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.pnct11.n.log 1.0986123
## D.ratio.weight.sum.wrds.n 5.6570206
## D.weight.post.stop.sum 6.6211185
## sprice.d20nexp 0.0192547
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.pnct11.n.log 1.6094379
## D.ratio.weight.sum.wrds.n 5.6570206
## D.weight.post.stop.sum 6.9916311
## sprice.d20nexp 0.9995001
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.pnct11.n.log 0.000000e+00
## D.ratio.weight.sum.wrds.n 0.000000e+00
## D.weight.post.stop.sum 0.000000e+00
## sprice.d20nexp 1.407565e-16
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.pnct11.n.log 0.000000e+00
## D.ratio.weight.sum.wrds.n 0.000000e+00
## D.weight.post.stop.sum 0.000000e+00
## sprice.d20nexp 7.785024e-20
## [1] "OOBobs total range outliers: 14"
## [1] "newobs sold.fctr.predict.Final.Ensemble.glmnet Y: min < min of Train range: 1"
## UniqueID sold.fctr.predict.Final.Ensemble.glmnet sprice.d20nexp
## 1154 12503 Y 7.785024e-20
## id cor.y exclude.as.feat cor.y.abs
## sprice.d20nexp sprice.d20nexp 0.3243319 FALSE 0.3243319
## cor.high.X freqRatio percentUnique zeroVar nzv
## sprice.d20nexp sprice.log10 3.560976 24.67066 FALSE FALSE
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## sprice.d20nexp FALSE <NA> 1.092982e-38
## rsp_var_raw id_var rsp_var max min
## sprice.d20nexp FALSE NA NA 0.9995001 7.785024e-20
## max.sold.fctr.N max.sold.fctr.Y min.sold.fctr.N
## sprice.d20nexp 0.9517052 0.9995001 4.248354e-18
## min.sold.fctr.Y max.sold.fctr.predict.Ensemble.glmnet.N
## sprice.d20nexp 7.681205e-15 0.9517052
## max.sold.fctr.predict.Ensemble.glmnet.Y
## sprice.d20nexp 0.9995001
## min.sold.fctr.predict.Ensemble.glmnet.N
## sprice.d20nexp 1.407565e-16
## min.sold.fctr.predict.Ensemble.glmnet.Y
## sprice.d20nexp 3.266131e-13
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## sprice.d20nexp 0.0192547
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## sprice.d20nexp 0.9995001
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## sprice.d20nexp 1.407565e-16
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## sprice.d20nexp 7.785024e-20
## [1] "newobs sold.fctr.predict.Final.Ensemble.glmnet Y: max > max of Train range: 5"
## UniqueID sold.fctr.predict.Final.Ensemble.glmnet D.chrs.n.log
## 930 12031 Y 4.663439
## 947 12059 Y 3.737670
## 967 12087 Y 4.663439
## 1108 12340 Y 4.624973
## 1154 12503 Y 0.000000
## D.chrs.pnct13.n.log D.wrds.stop.n.log sprice.log10 sprice.root2
## 930 0.6931472 1.386294 0.00000000 1.0000000
## 947 2.7080502 0.000000 4.94164242 11.8321596
## 967 0.0000000 1.098612 -0.01005034 0.9949874
## 1108 0.0000000 2.564949 -0.01005034 0.9949874
## 1154 0.0000000 0.000000 6.77991054 29.6646254
## id cor.y exclude.as.feat
## D.chrs.n.log D.chrs.n.log 0.02239267 FALSE
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log 0.02248931 FALSE
## D.wrds.stop.n.log D.wrds.stop.n.log 0.04476033 FALSE
## sprice.log10 sprice.log10 -0.37729587 FALSE
## sprice.root2 sprice.root2 -0.46922279 FALSE
## cor.y.abs cor.high.X freqRatio percentUnique
## D.chrs.n.log 0.02239267 <NA> 16.645161 9.8203593
## D.chrs.pnct13.n.log 0.02248931 <NA> 6.196078 0.9580838
## D.wrds.stop.n.log 0.04476033 <NA> 9.417910 1.1976048
## sprice.log10 0.37729587 sprice.root2 3.560976 24.6706587
## sprice.root2 0.46922279 <NA> 3.560976 24.6706587
## zeroVar nzv is.cor.y.abs.low interaction.feat
## D.chrs.n.log FALSE FALSE TRUE <NA>
## D.chrs.pnct13.n.log FALSE FALSE TRUE <NA>
## D.wrds.stop.n.log FALSE FALSE FALSE <NA>
## sprice.log10 FALSE FALSE FALSE <NA>
## sprice.root2 FALSE FALSE FALSE <NA>
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.chrs.n.log 2.319325e-37 FALSE NA NA
## D.chrs.pnct13.n.log 1.229832e-40 FALSE NA NA
## D.wrds.stop.n.log 1.227574e-40 FALSE NA NA
## sprice.log10 7.580418e-30 FALSE NA NA
## sprice.root2 5.910306e-16 FALSE NA NA
## max min max.sold.fctr.N max.sold.fctr.Y
## D.chrs.n.log 4.663439 0.00000 4.634729 4.634729
## D.chrs.pnct13.n.log 2.708050 0.00000 1.386294 2.197225
## D.wrds.stop.n.log 2.564949 0.00000 2.197225 2.302585
## sprice.log10 6.779911 -4.60517 6.684612 6.476972
## sprice.root2 29.664625 0.10000 28.284271 25.495098
## min.sold.fctr.N min.sold.fctr.Y
## D.chrs.n.log 0.00000000 0.00000
## D.chrs.pnct13.n.log 0.00000000 0.00000
## D.wrds.stop.n.log 0.00000000 0.00000
## sprice.log10 -0.01005034 -4.60517
## sprice.root2 0.99498744 0.10000
## max.sold.fctr.predict.Ensemble.glmnet.N
## D.chrs.n.log 4.615121
## D.chrs.pnct13.n.log 1.386294
## D.wrds.stop.n.log 2.079442
## sprice.log10 6.593031
## sprice.root2 27.018327
## max.sold.fctr.predict.Ensemble.glmnet.Y
## D.chrs.n.log 4.624973
## D.chrs.pnct13.n.log 1.791759
## D.wrds.stop.n.log 2.197225
## sprice.log10 6.354370
## sprice.root2 23.979158
## min.sold.fctr.predict.Ensemble.glmnet.N
## D.chrs.n.log 0.00000000
## D.chrs.pnct13.n.log 0.00000000
## D.wrds.stop.n.log 0.00000000
## sprice.log10 -0.01005034
## sprice.root2 0.99498744
## min.sold.fctr.predict.Ensemble.glmnet.Y
## D.chrs.n.log 0.00000
## D.chrs.pnct13.n.log 0.00000
## D.wrds.stop.n.log 0.00000
## sprice.log10 -4.60517
## sprice.root2 0.10000
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.n.log 4.615121
## D.chrs.pnct13.n.log 1.791759
## D.wrds.stop.n.log 1.386294
## sprice.log10 6.593031
## sprice.root2 27.018327
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.n.log 4.663439
## D.chrs.pnct13.n.log 2.708050
## D.wrds.stop.n.log 2.564949
## sprice.log10 6.779911
## sprice.root2 29.664625
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.n.log 0.000000
## D.chrs.pnct13.n.log 0.000000
## D.wrds.stop.n.log 0.000000
## sprice.log10 4.369448
## sprice.root2 8.888194
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.n.log 0.00000
## D.chrs.pnct13.n.log 0.00000
## D.wrds.stop.n.log 0.00000
## sprice.log10 -4.60517
## sprice.root2 0.10000
## [1] "newobs sold.fctr.predict.Final.Ensemble.glmnet N: min < min of Train range: 2"
## UniqueID sold.fctr.predict.Final.Ensemble.glmnet
## 1003 12141 N
## 1129 12429 N
## D.ratio.wrds.stop.n.wrds.n D.weight.sum.stem.stop.Ratio
## 1003 0.0500000 0.9505180
## 1129 0.3333333 0.8048203
## id cor.y
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n -0.01535889
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio -0.05172679
## exclude.as.feat cor.y.abs cor.high.X
## D.ratio.wrds.stop.n.wrds.n FALSE 0.01535889 <NA>
## D.weight.sum.stem.stop.Ratio FALSE 0.05172679 <NA>
## freqRatio percentUnique zeroVar nzv
## D.ratio.wrds.stop.n.wrds.n 18.46429 7.784431 FALSE FALSE
## D.weight.sum.stem.stop.Ratio 89.16667 32.215569 FALSE FALSE
## is.cor.y.abs.low interaction.feat
## D.ratio.wrds.stop.n.wrds.n TRUE <NA>
## D.weight.sum.stem.stop.Ratio FALSE <NA>
## shapiro.test.p.value rsp_var_raw id_var
## D.ratio.wrds.stop.n.wrds.n 9.913172e-37 FALSE NA
## D.weight.sum.stem.stop.Ratio 3.397141e-41 FALSE NA
## rsp_var max min max.sold.fctr.N
## D.ratio.wrds.stop.n.wrds.n NA 1 0.04545455 1
## D.weight.sum.stem.stop.Ratio NA 1 0.71178250 1
## max.sold.fctr.Y min.sold.fctr.N
## D.ratio.wrds.stop.n.wrds.n 1 0.05263158
## D.weight.sum.stem.stop.Ratio 1 0.85910378
## min.sold.fctr.Y
## D.ratio.wrds.stop.n.wrds.n 0.04545455
## D.weight.sum.stem.stop.Ratio 0.71178250
## max.sold.fctr.predict.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## max.sold.fctr.predict.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## min.sold.fctr.predict.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 0.05263158
## D.weight.sum.stem.stop.Ratio 0.71178250
## min.sold.fctr.predict.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 0.04545455
## D.weight.sum.stem.stop.Ratio 0.81284882
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 0.0500000
## D.weight.sum.stem.stop.Ratio 0.8048203
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 0.05263158
## D.weight.sum.stem.stop.Ratio 0.77042467
## [1] "newobs sold.fctr.predict.Final.Ensemble.glmnet N: max > max of Train range: 1"
## UniqueID sold.fctr.predict.Final.Ensemble.glmnet D.chrs.pnct13.n.log
## 950 12063 N 1.791759
## id cor.y exclude.as.feat
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log 0.02248931 FALSE
## cor.y.abs cor.high.X freqRatio percentUnique zeroVar
## D.chrs.pnct13.n.log 0.02248931 <NA> 6.196078 0.9580838 FALSE
## nzv is.cor.y.abs.low interaction.feat
## D.chrs.pnct13.n.log FALSE TRUE <NA>
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.chrs.pnct13.n.log 1.229832e-40 FALSE NA NA
## max min max.sold.fctr.N max.sold.fctr.Y
## D.chrs.pnct13.n.log 2.70805 0 1.386294 2.197225
## min.sold.fctr.N min.sold.fctr.Y
## D.chrs.pnct13.n.log 0 0
## max.sold.fctr.predict.Ensemble.glmnet.N
## D.chrs.pnct13.n.log 1.386294
## max.sold.fctr.predict.Ensemble.glmnet.Y
## D.chrs.pnct13.n.log 1.791759
## min.sold.fctr.predict.Ensemble.glmnet.N
## D.chrs.pnct13.n.log 0
## min.sold.fctr.predict.Ensemble.glmnet.Y
## D.chrs.pnct13.n.log 0
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.pnct13.n.log 1.791759
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.pnct13.n.log 2.70805
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.pnct13.n.log 0
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.pnct13.n.log 0
## [1] "newobs total range outliers: 8"out_fname <- paste0(glb_out_pfx, "out.csv")
write.csv(obsout_df, out_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glbFeatsText) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words term weights for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
terms_mtrx <- glb_post_stem_words_terms_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(terms_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glbFeatsText)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(terms_mtrx[, 207] > 0), c(glb_id_var, glbFeatsText)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glbFeatsText)]
#terms_mtrx[2442, terms_mtrx[2442, ] > 0]
print(sprintf(" All post-stem-words term freq distribution for %s:", txt_var))
print(table(glb_post_stem_words_terms_df_lst[[txt_var]]$freq))
print(sprintf(" All post-stem-words term length distribution for %s:", txt_var))
print(table(nchar(glb_post_stem_words_terms_df_lst[[txt_var]]$term)))
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], nchar(term) >= 10))
print(sprintf(" Analyzed term weights for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
anl_terms_vctr <- union(select_terms, assoc_terms)
print(subset(tmp_df, term %in% anl_terms_vctr))
# tmp_freq1_df <- subset(tmp_df, freq == 1)
# tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
# print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words term weights for descr.my:"
## term weight freq pos cor.y cor.y.abs chisq.stat
## condit condit 78.89912 159 50 0.001876192 0.001876192 32.693036
## likenew likenew 56.21589 24 111 -0.065514942 0.065514942 8.821196
## use use 51.89835 123 200 0.016242616 0.016242616 32.201419
## excel excel 46.05261 46 68 0.048677717 0.048677717 8.245331
## good good 43.62859 67 83 0.016953140 0.016953140 13.151429
## and and 42.42133 130 19 0.006615581 0.006615581 19.047232
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y weight.N
## condit 0.04973364 66.54545 2.634731 TRUE 36.29141 11.028663
## likenew 0.45394058 273.66667 1.197605 TRUE 14.90115 16.360991
## use 0.09602654 83.11111 2.874251 TRUE 27.96807 7.123860
## excel 0.82725815 202.50000 1.676647 TRUE 27.19181 1.730088
## good 0.72599123 131.00000 2.155689 TRUE 25.23756 6.040657
## and 0.64241004 45.87500 2.754491 TRUE 24.09867 7.080998
## weight.NA
## condit 31.57904
## likenew 24.95375
## use 16.80641
## excel 17.13071
## good 12.35037
## and 11.24166
## term weight freq pos cor.y cor.y.abs chisq.stat
## not not 18.275074 19 130 0.0254781613 0.0254781613 10.9602312
## sign sign 12.586604 32 174 0.0006235094 0.0006235094 13.7490132
## blemish blemish 5.688081 7 33 0.0061216455 0.0061216455 3.8567029
## first first 3.514651 6 71 -0.0553159186 0.0553159186 3.4039230
## tablet tablet 3.082091 6 186 0.0080263755 0.0080263755 0.7756667
## gentl gentl 2.164255 3 81 0.0258276205 0.0258276205 0.6190376
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y
## not 0.4466010 273.0000 1.4371257 TRUE 13.702228
## sign 0.2471940 161.8000 1.4371257 TRUE 8.102393
## blemish 0.2773592 832.0000 0.4790419 TRUE 2.354299
## first 0.1823255 277.0000 0.3592814 TRUE 0.900826
## tablet 0.8552779 276.6667 0.4790419 TRUE 2.014526
## gentl 0.7338000 833.0000 0.3592814 TRUE 1.623191
## weight.N weight.NA
## not 1.5520323 3.0208138
## sign 2.4797107 2.0045002
## blemish 0.5310449 2.8027368
## first 1.3011931 1.3126321
## tablet 0.4785638 0.5890016
## gentl 0.0000000 0.5410638
## term weight freq pos cor.y cor.y.abs chisq.stat
## b b 0.7315702 1 26 0.01924154 0.01924154 2.73274e-30
## 4g 4g 0.6401239 1 8 0.01924154 0.01924154 2.73274e-30
## heavili heavili 0.6401239 1 88 0.01924154 0.01924154 2.73274e-30
## ipad3 ipad3 0.6401239 1 98 0.01924154 0.01924154 2.73274e-30
## ship ship 0.6401239 1 171 0.01924154 0.01924154 2.73274e-30
## seller seller 0.5390517 1 168 0.01924154 0.01924154 2.73274e-30
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y weight.N
## b 1 834 0.239521 TRUE 0.7315702 0
## 4g 1 834 0.239521 TRUE 0.6401239 0
## heavili 1 834 0.239521 TRUE 0.6401239 0
## ipad3 1 834 0.239521 TRUE 0.6401239 0
## ship 1 834 0.239521 TRUE 0.6401239 0
## seller 1 834 0.239521 TRUE 0.5390517 0
## weight.NA
## b 0
## 4g 0
## heavili 0
## ipad3 0
## ship 0
## seller 0
## UniqueID
## 339 10579
## descr.my
## 339 This is a Seller refurbished item and will show signs of cosmetic use. The unit will be 100% working
## [1] 16
## [1] " All post-stem-words term freq distribution for descr.my:"
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 16 6 8 15 18 12 19 13 3 3 4 8 9 4 6 7 4 2
## 19 20 21 22 23 24 25 26 30 31 32 33 34 35 36 37 38 39
## 3 1 2 1 1 1 5 1 3 1 2 1 1 2 2 1 1 1
## 41 42 44 45 46 47 48 50 54 60 67 68 69 73 74 77 79 83
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 84 100 106 118 123 130 141 159
## 1 1 2 1 1 1 1 1
## [1] " All post-stem-words term length distribution for descr.my:"
##
## 1 2 3 4 5 6 7 8 9 10
## 5 16 28 62 49 27 14 8 5 1
## term weight freq pos cor.y cor.y.abs chisq.stat
## manufactur manufactur 5.890041 5 117 0.01373197 0.01373197 4.164932
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y
## manufactur 0.3841446 831 0.5988024 TRUE 4.240029
## weight.N weight.NA
## manufactur 0.6600046 0.9900069
## [1] " Analyzed term weights for descr.my:"
## term weight freq pos cor.y cor.y.abs chisq.stat
## condit condit 78.899117 159 50 0.001876192 0.001876192 32.6930364
## mint mint 27.069148 19 122 -0.039506388 0.039506388 13.1409711
## for for 20.222122 41 73 -0.032982618 0.032982618 26.5189036
## read read 8.895815 17 153 -0.039507937 0.039507937 12.8231940
## wifi wifi 8.579641 15 209 -0.057666826 0.057666826 10.6880158
## full full 5.640632 9 77 -0.120935138 0.120935138 12.8894084
## usb usb 5.067053 7 199 0.023641053 0.023641053 4.7836215
## 32gb 32gb 5.021210 8 7 -0.065561835 0.065561835 7.8851891
## part part 3.647219 5 145 -0.041612512 0.041612512 6.7970683
## unlock unlock 3.296793 4 197 0.033367352 0.033367352 0.0801224
## adapt adapt 2.743733 5 13 -0.060145549 0.060145549 10.3554383
## at at 2.106285 4 24 -0.070184018 0.070184018 10.0527397
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.Y
## condit 0.04973364 66.54545 2.6347305 TRUE 36.2914145
## mint 0.21589645 411.00000 1.3173653 TRUE 7.3741995
## for 0.04715022 161.80000 2.0359281 TRUE 7.9844521
## read 0.17077111 273.33333 1.1976048 TRUE 5.0593843
## wifi 0.15281842 274.66667 0.9580838 TRUE 3.5748022
## full 0.01182900 276.33333 0.5988024 TRUE 0.5893382
## usb 0.44285403 414.50000 0.7185629 TRUE 3.8589261
## 32gb 0.16267763 414.50000 0.7185629 TRUE 1.6674818
## part 0.03342223 416.00000 0.3592814 TRUE 0.7920055
## unlock 0.77713161 277.33333 0.2395210 TRUE 2.4725949
## adapt 0.03484764 415.00000 0.5988024 TRUE 1.1880083
## at 0.03954860 831.00000 0.5988024 TRUE 0.6868319
## weight.N weight.NA
## condit 11.0286629 31.5790396
## mint 5.3092144 14.3857335
## for 3.8242572 8.4134130
## read 2.8839451 0.9524853
## wifi 2.9685594 2.0362797
## full 3.3138939 1.7373996
## usb 0.4646643 0.7434628
## 32gb 2.2329448 1.1207831
## part 0.9317712 1.9234419
## unlock 0.0000000 0.8241983
## adapt 1.5557251 0.0000000
## at 1.4194527 0.0000000if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.2print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Ensemble.glmnet"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.Ensemble.glmnet"get_dsp_models_df()## [1] "Cross Validation issues:"## Warning in get_dsp_models_df(): Cross Validation issues:## MFO.myMFO_classfr Random.myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.cp.0.rpart
## 0 0## id
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.Interact.glmnet Max.cor.Y.rcv.3X5.Interact.glmnet
## Max.cor.Y.rcv.3X5.center.scale.glmnet Max.cor.Y.rcv.3X5.center.scale.glmnet
## Max.cor.Y.rcv.3X5.range.glmnet Max.cor.Y.rcv.3X5.range.glmnet
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet Max.cor.Y.rcv.3X5.YeoJohnson.glmnet
## Max.cor.Y.rcv.3X5.spatialSign.glmnet Max.cor.Y.rcv.3X5.spatialSign.glmnet
## RFE.X.nnet RFE.X.nnet
## RFE.X.bayesglm RFE.X.bayesglm
## RFE.X.rf RFE.X.rf
## RFE.X.earth RFE.X.earth
## RFE.X.avNNet RFE.X.avNNet
## Low.cor.X.glmnet Low.cor.X.glmnet
## Max.cor.Y.rcv.3X5.pca.glmnet Max.cor.Y.rcv.3X5.pca.glmnet
## RFE.X.glmnet RFE.X.glmnet
## All.X.glmnet All.X.glmnet
## RFE.X.gbm RFE.X.gbm
## Ensemble.glmnet Ensemble.glmnet
## RFE.X.glm RFE.X.glm
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rcv.3X5.ica.glmnet Max.cor.Y.rcv.3X5.ica.glmnet
## Max.cor.Y.rpart Max.cor.Y.rpart
## RFE.X.rpart RFE.X.rpart
## Random.myrandom_classfr Random.myrandom_classfr
## MFO.myMFO_classfr MFO.myMFO_classfr
## Final.Ensemble.glmnet Final.Ensemble.glmnet
## Final.Ensemble.glm Final.Ensemble.glm
## RFE.X.Train.avNNet RFE.X.Train.avNNet
## RFE.X.Train.glmnet RFE.X.Train.glmnet
## RFE.X.Train.earth RFE.X.Train.earth
## RFE.X.Train.nnet RFE.X.Train.nnet
## RFE.X.Train.rpart RFE.X.Train.rpart
## RFE.X.Train.glm RFE.X.Train.glm
## RFE.X.Train.gbm RFE.X.Train.gbm
## max.AUCROCR.OOB max.AUCpROC.OOB
## Max.cor.Y.rcv.3X5.glmnet 0.8749132 0.7486978
## Max.cor.Y.rcv.5X5.glmnet 0.8747974 0.7486978
## Max.cor.Y.rcv.3X1.glmnet 0.8747106 0.7486978
## Max.cor.Y.rcv.5X3.glmnet 0.8747106 0.7486978
## Max.cor.Y.rcv.3X3.glmnet 0.8747106 0.7486978
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.8747106 0.7486978
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8747106 0.7486978
## Max.cor.Y.rcv.3X5.range.glmnet 0.8747106 0.7486978
## Interact.High.cor.Y.glmnet 0.8736399 0.7205116
## Max.cor.Y.rcv.1X1.glmnet 0.8730032 0.7471640
## Max.cor.Y.rcv.5X1.glmnet 0.8730032 0.7471640
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8712669 0.7471640
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8697911 0.7486978
## RFE.X.nnet 0.8596336 0.7367751
## RFE.X.bayesglm 0.8568411 0.7304086
## RFE.X.rf 0.8557414 0.7465563
## RFE.X.earth 0.8513283 0.6958560
## RFE.X.avNNet 0.8506048 0.7163734
## Low.cor.X.glmnet 0.8467126 0.6734576
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8420679 0.6610719
## RFE.X.glmnet 0.8419956 0.6813578
## All.X.glmnet 0.8419956 0.6813578
## RFE.X.gbm 0.8382625 0.6785218
## Ensemble.glmnet 0.8277578 0.7134217
## RFE.X.glm 0.8206390 0.7009202
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8205087 0.6697824
## Max.cor.Y.rcv.3X5.ica.glmnet 0.7595642 0.5229772
## Max.cor.Y.rpart 0.7542540 0.7600995
## RFE.X.rpart 0.7542540 0.7600995
## Random.myrandom_classfr 0.5310800 0.5212988
## MFO.myMFO_classfr 0.5000000 0.5000000
## Final.Ensemble.glmnet NA NA
## Final.Ensemble.glm NA NA
## RFE.X.Train.avNNet NA NA
## RFE.X.Train.glmnet NA NA
## RFE.X.Train.earth NA NA
## RFE.X.Train.nnet NA NA
## RFE.X.Train.rpart NA NA
## RFE.X.Train.glm NA NA
## RFE.X.Train.gbm NA NA
## max.Accuracy.OOB max.Accuracy.fit
## Max.cor.Y.rcv.3X5.glmnet 0.8657407 0.8531306
## Max.cor.Y.rcv.5X5.glmnet 0.8657407 0.8541198
## Max.cor.Y.rcv.3X1.glmnet 0.8657407 0.8561083
## Max.cor.Y.rcv.5X3.glmnet 0.8657407 0.8536089
## Max.cor.Y.rcv.3X3.glmnet 0.8657407 0.8528223
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.8657407 0.8528223
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.8657407 0.8528223
## Max.cor.Y.rcv.3X5.range.glmnet 0.8657407 0.8528223
## Interact.High.cor.Y.glmnet 0.8611111 0.8544868
## Max.cor.Y.rcv.1X1.glmnet 0.8634259 0.8511166
## Max.cor.Y.rcv.5X1.glmnet 0.8634259 0.8510426
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.8634259 0.8528223
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.8657407 0.8586021
## RFE.X.nnet 0.8333333 0.8221854
## RFE.X.bayesglm 0.8333333 0.8337694
## RFE.X.rf 0.8171296 0.8188586
## RFE.X.earth 0.8356481 0.8204901
## RFE.X.avNNet 0.8356481 0.8370616
## Low.cor.X.glmnet 0.8194444 0.8412075
## Max.cor.Y.rcv.3X5.pca.glmnet 0.8194444 0.8263313
## RFE.X.glmnet 0.8217593 0.8445059
## All.X.glmnet 0.8217593 0.8445059
## RFE.X.gbm 0.8078704 0.8221792
## Ensemble.glmnet 0.8148148 0.9321786
## RFE.X.glm 0.7986111 0.7824765
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7754630 0.8585608
## Max.cor.Y.rcv.3X5.ica.glmnet 0.7546296 0.7791720
## Max.cor.Y.rpart 0.7546296 0.8156317
## RFE.X.rpart 0.7546296 0.8147595
## Random.myrandom_classfr 0.7546296 0.7741935
## MFO.myMFO_classfr 0.7546296 0.7741935
## Final.Ensemble.glmnet NA 0.9223682
## Final.Ensemble.glm NA 0.9219657
## RFE.X.Train.avNNet NA 0.8490780
## RFE.X.Train.glmnet NA 0.8478890
## RFE.X.Train.earth NA 0.8450754
## RFE.X.Train.nnet NA 0.8387078
## RFE.X.Train.rpart NA 0.8322942
## RFE.X.Train.glm NA 0.8303159
## RFE.X.Train.gbm NA 0.8291082
## opt.prob.threshold.fit
## Max.cor.Y.rcv.3X5.glmnet 0.4
## Max.cor.Y.rcv.5X5.glmnet 0.4
## Max.cor.Y.rcv.3X1.glmnet 0.4
## Max.cor.Y.rcv.5X3.glmnet 0.4
## Max.cor.Y.rcv.3X3.glmnet 0.4
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.4
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.4
## Max.cor.Y.rcv.3X5.range.glmnet 0.4
## Interact.High.cor.Y.glmnet 0.5
## Max.cor.Y.rcv.1X1.glmnet 0.4
## Max.cor.Y.rcv.5X1.glmnet 0.4
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.4
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5
## RFE.X.nnet 0.6
## RFE.X.bayesglm 0.5
## RFE.X.rf 0.6
## RFE.X.earth 0.4
## RFE.X.avNNet 0.6
## Low.cor.X.glmnet 0.5
## Max.cor.Y.rcv.3X5.pca.glmnet 0.5
## RFE.X.glmnet 0.5
## All.X.glmnet 0.5
## RFE.X.gbm 0.5
## Ensemble.glmnet 0.5
## RFE.X.glm 0.5
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.5
## Max.cor.Y.rcv.3X5.ica.glmnet 0.1
## Max.cor.Y.rpart 0.9
## RFE.X.rpart 0.9
## Random.myrandom_classfr 0.2
## MFO.myMFO_classfr 0.2
## Final.Ensemble.glmnet 0.4
## Final.Ensemble.glm 0.3
## RFE.X.Train.avNNet 0.5
## RFE.X.Train.glmnet 0.5
## RFE.X.Train.earth 0.4
## RFE.X.Train.nnet 0.6
## RFE.X.Train.rpart 0.5
## RFE.X.Train.glm 0.5
## RFE.X.Train.gbm 0.5
## opt.prob.threshold.OOB
## Max.cor.Y.rcv.3X5.glmnet 0.5
## Max.cor.Y.rcv.5X5.glmnet 0.5
## Max.cor.Y.rcv.3X1.glmnet 0.5
## Max.cor.Y.rcv.5X3.glmnet 0.5
## Max.cor.Y.rcv.3X3.glmnet 0.5
## Max.cor.Y.rcv.3X5.Interact.glmnet 0.5
## Max.cor.Y.rcv.3X5.center.scale.glmnet 0.5
## Max.cor.Y.rcv.3X5.range.glmnet 0.5
## Interact.High.cor.Y.glmnet 0.6
## Max.cor.Y.rcv.1X1.glmnet 0.5
## Max.cor.Y.rcv.5X1.glmnet 0.5
## Max.cor.Y.rcv.3X5.YeoJohnson.glmnet 0.5
## Max.cor.Y.rcv.3X5.spatialSign.glmnet 0.5
## RFE.X.nnet 0.4
## RFE.X.bayesglm 0.5
## RFE.X.rf 0.3
## RFE.X.earth 0.6
## RFE.X.avNNet 0.4
## Low.cor.X.glmnet 0.5
## Max.cor.Y.rcv.3X5.pca.glmnet 0.6
## RFE.X.glmnet 0.5
## All.X.glmnet 0.5
## RFE.X.gbm 0.5
## Ensemble.glmnet 0.2
## RFE.X.glm 0.3
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.2
## Max.cor.Y.rcv.3X5.ica.glmnet 0.1
## Max.cor.Y.rpart 0.3
## RFE.X.rpart 0.3
## Random.myrandom_classfr 0.2
## MFO.myMFO_classfr 0.2
## Final.Ensemble.glmnet NA
## Final.Ensemble.glm NA
## RFE.X.Train.avNNet NA
## RFE.X.Train.glmnet NA
## RFE.X.Train.earth NA
## RFE.X.Train.nnet NA
## RFE.X.Train.rpart NA
## RFE.X.Train.glm NA
## RFE.X.Train.gbm NAif (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
#stop(here"); glb_to_sav(); glb_ctgry_df <- sav_ctgry_df
# OOB_ctgry_df <- myget_category_stats(glb_OOBobs_df, glb_sel_mdl_id, "OOB")
# glb_ctgry_df <- merge(glb_ctgry_df, subset(OOB_ctgry_df, select=-.n.OOB),
# by=glb_category_var, all=TRUE)
#
# #glb_fitobs_df <- glb_get_predictions(glb_fitobs_df, glb_sel_mdl_id, glb_rsp_var_out)
# glb_ctgry_df <- merge(glb_ctgry_df,
# myget_category_stats(obs_df=glb_fitobs_df, mdl_id=glb_sel_mdl_id, label="fit"),
# by=glb_category_var, all=TRUE)
# row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, label="trn"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id, label="new"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))
print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
mdl_id=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, label="trn"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id, label="new"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))
print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
# print("Top category OOB errors:")
# print(glb_OOBobs_df[(glb_OOBobs_df[, glb_category_var] ==
# dsp_ctgry_df[1, glb_category_var]) &
# !glb_OOBobs_df[, predct_accurate_var_name],
# c(glb_id_var, glb_rsp_var_raw, paste0(glb_rsp_var_out, glb_sel_mdl_id),
# glb_category_var,
# row.names(head(myget_feats_importance(glb_sel_mdl), 5)),
# # "biddable", "startprice", "condition",
# glbFeatsText)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "Ensemble.glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 45 61
## Y 19 307
## prdl.descr.my.fctr .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## iPadmini3#1 iPadmini3#1 4 3 0.007978723 0.009259259
## iPadmini2#1 iPadmini2#1 15 13 0.034574468 0.034722222
## iPadmini#1 iPadmini#1 24 20 0.053191489 0.055555556
## iPadmini3#0 iPadmini3#0 13 11 0.029255319 0.030092593
## Unknown#1 Unknown#1 28 24 0.063829787 0.064814815
## Unknown#0 Unknown#0 16 14 0.037234043 0.037037037
## iPad1#1 iPad1#1 23 20 0.053191489 0.053240741
## iPad3#1 iPad3#1 7 5 0.013297872 0.016203704
## iPad4#1 iPad4#1 17 15 0.039893617 0.039351852
## iPad4#0 iPad4#0 13 11 0.029255319 0.030092593
## iPadmini2#0 iPadmini2#0 24 21 0.055851064 0.055555556
## iPad2#1 iPad2#1 21 18 0.047872340 0.048611111
## iPadAir2#0 iPadAir2#0 21 19 0.050531915 0.048611111
## iPadmini#0 iPadmini#0 44 39 0.103723404 0.101851852
## iPad3#0 iPad3#0 21 18 0.047872340 0.048611111
## iPadAir#1 iPadAir#1 16 14 0.037234043 0.037037037
## iPad1#0 iPad1#0 34 30 0.079787234 0.078703704
## iPad2#0 iPad2#0 58 52 0.138297872 0.134259259
## iPadAir#0 iPadAir#0 25 22 0.058510638 0.057870370
## iPadAir2#1 iPadAir2#1 8 7 0.018617021 0.018518519
## err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## iPadmini3#1 0.01122104 0.01122104 1 1.709065
## iPadmini2#1 0.03149610 0.01574805 2 6.240199
## iPadmini#1 2.39469879 0.07982329 30 6.903829
## iPadmini3#0 0.54235118 0.04519593 12 3.539873
## Unknown#1 0.05844056 0.01461014 4 7.020221
## Unknown#0 6.78540300 0.18848342 36 3.802775
## iPad1#1 3.57274702 0.12759811 28 5.415330
## iPad3#1 1.68087992 0.09338222 18 1.607253
## iPad4#1 0.75385034 0.08376115 9 3.777315
## iPad4#0 1.51381325 0.06055253 25 2.748442
## iPadmini2#0 0.45051170 0.09010234 5 5.047830
## iPad2#1 2.48585383 0.06905150 36 4.255439
## iPadAir2#0 4.43222740 0.16415657 27 3.981589
## iPadmini#0 5.99909945 0.15787104 38 8.083408
## iPad3#0 3.59127017 0.11584742 31 3.834528
## iPadAir#1 1.23704849 0.06872492 18 2.862180
## iPad1#0 1.66067119 0.05032337 33 6.065565
## iPad2#0 2.30394852 0.09215794 25 10.130293
## iPadAir#0 0.76215620 0.05081041 15 3.903209
## iPadAir2#1 1.48628213 0.14862821 10 1.002698
## err.abs.OOB.mean err.abs.trn.sum err.abs.trn.mean .n.trn
## iPadmini3#1 0.4272664 0.112983 0.02259659 5
## iPadmini2#1 0.4160133 1.335071 0.07853359 17
## iPadmini#1 0.2876595 5.375947 0.09955457 54
## iPadmini3#0 0.2722979 4.020644 0.16082576 25
## Unknown#1 0.2507222 3.423172 0.10697411 32
## Unknown#0 0.2376734 9.125729 0.17549479 52
## iPad1#1 0.2354491 4.990778 0.09785839 51
## iPad3#1 0.2296076 2.481980 0.09927919 25
## iPad4#1 0.2221950 1.419344 0.05459016 26
## iPad4#0 0.2114186 4.506872 0.11860190 38
## iPadmini2#0 0.2103262 4.660803 0.16071735 29
## iPad2#1 0.2026400 4.153237 0.07286380 57
## iPadAir2#0 0.1895995 8.800394 0.18334154 48
## iPadmini#0 0.1837138 15.236982 0.18581685 82
## iPad3#0 0.1825966 7.630785 0.14674586 52
## iPadAir#1 0.1788862 1.379342 0.04056888 34
## iPad1#0 0.1783990 5.449822 0.08134062 67
## iPad2#0 0.1746602 7.904608 0.09523624 83
## iPadAir#0 0.1561283 2.906069 0.07265172 40
## iPadAir2#1 0.1253372 2.140674 0.11892633 18
## err.abs.new.sum err.abs.new.mean .n.new
## iPadmini3#1 NA NA 3
## iPadmini2#1 NA NA 13
## iPadmini#1 NA NA 20
## iPadmini3#0 NA NA 11
## Unknown#1 NA NA 24
## Unknown#0 NA NA 14
## iPad1#1 NA NA 20
## iPad3#1 NA NA 5
## iPad4#1 NA NA 15
## iPad4#0 NA NA 11
## iPadmini2#0 NA NA 21
## iPad2#1 NA NA 18
## iPadAir2#0 NA NA 19
## iPadmini#0 NA NA 39
## iPad3#0 NA NA 18
## iPadAir#1 NA NA 14
## iPad1#0 NA NA 30
## iPad2#0 NA NA 52
## iPadAir#0 NA NA 22
## iPadAir2#1 NA NA 7
## .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## 432.000000 376.000000 1.000000 1.000000
## err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## 41.753970 1.728050 403.000000 91.931041
## err.abs.OOB.mean err.abs.trn.sum err.abs.trn.mean .n.trn
## 4.572590 97.055234 2.172518 835.000000
## err.abs.new.sum err.abs.new.mean .n.new
## NA NA 376.000000if (!is.null(glb_featsimp_df))
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")),
subset(glb_featsimp_df, importance > 10)))## Ensemble.glmnet.importance
## sold.fctr.predict.RFE.X.Train.gbm.prob NA
## sold.fctr.predict.RFE.X.Train.glm.prob NA
## sold.fctr.predict.RFE.X.Train.avNNet.prob NA
## sold.fctr.predict.RFE.X.Train.earth.prob NA
## sold.fctr.predict.RFE.X.Train.nnet.prob NA
## sold.fctr.predict.RFE.X.Train.rpart.prob NA
## importance
## sold.fctr.predict.RFE.X.Train.gbm.prob 100.00000
## sold.fctr.predict.RFE.X.Train.glm.prob 57.62471
## sold.fctr.predict.RFE.X.Train.avNNet.prob 49.18183
## sold.fctr.predict.RFE.X.Train.earth.prob 38.73069
## sold.fctr.predict.RFE.X.Train.nnet.prob 38.73069
## sold.fctr.predict.RFE.X.Train.rpart.prob 28.94300
## Final.Ensemble.glmnet.importance
## sold.fctr.predict.RFE.X.Train.gbm.prob 100.00000
## sold.fctr.predict.RFE.X.Train.glm.prob 57.62471
## sold.fctr.predict.RFE.X.Train.avNNet.prob 49.18183
## sold.fctr.predict.RFE.X.Train.earth.prob 38.73069
## sold.fctr.predict.RFE.X.Train.nnet.prob 38.73069
## sold.fctr.predict.RFE.X.Train.rpart.prob 28.94300print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"if (glb_is_regression)
print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))##
## N Y
## 65 311# Use this to see how prediction changes by changing one or more values
# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
# dsp_chisq.test(Headline.contains="[Vi]deo")
if ((length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)) {
print(diff)
stop("glb_*obs_df not in sync")
}
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file = paste0(glb_out_pfx, "prdnew_dsk.RData"))
sav_fin_mdl <- glb_fin_mdl; sav_sel_mdl <- glb_sel_mdl
#save(sav_fin_mdl, sav_sel_mdl, file=paste0(glb_out_pfx, "sav_mdl.RData"))
# load(file=paste0(glb_out_pfx, "sav_mdl_01.RData"), verbose=TRUE)
# prv_fin_mdl <- sav_fin_mdl; prv_sel_mdl <- sav_sel_mdl
# load(file=paste0(glb_out_pfx, "sav_mdl.RData"), verbose=TRUE)
# cur_fin_mdl <- sav_fin_mdl; cur_sel_mdl <- sav_sel_mdl
# all.equal(cur_fin_mdl, prv_fin_mdl)
# cur_fitobs_df <- cur_fin_mdl$trainingData; prv_fitobs_df <- prv_fin_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# nrow(cur_fitobs_df); nrow(prv_fitobs_df)
# names(cur_fitobs_df); names(prv_fitobs_df)
# all.equal(cur_fin_mdl$bestTune, prv_fin_mdl$bestTune)
# all.equal(glb_sel_mdl, sav_sel_mdl)
# cur_fitobs_df <- cur_sel_mdl$trainingData; prv_fitobs_df <- prv_sel_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# head(myget_feats_importance(glb_sel_mdl)); head(myget_feats_importance(sav_sel_mdl))
# head(myget_feats_importance(cur_sel_mdl)); head(myget_feats_importance(prv_sel_mdl))
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 16 predict.data.new 8 0 0 731.346 761.557
## 17 display.session.info 9 0 0 761.558 NA
## elapsed
## 16 30.211
## 17 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 11 fit.models 6 1 1 260.166
## 14 fit.data.training 7 0 0 541.552
## 5 extract.features 3 0 0 25.025
## 12 fit.models 6 2 2 459.616
## 10 fit.models 6 0 0 188.519
## 7 cluster.data 3 2 2 133.547
## 16 predict.data.new 8 0 0 731.346
## 9 select.features 5 0 0 170.030
## 1 import.data 1 0 0 8.933
## 15 fit.data.training 7 1 1 721.652
## 13 fit.models 6 3 3 532.108
## 2 inspect.data 2 0 0 18.809
## 3 scrub.data 2 1 1 23.654
## 8 partition.data.training 4 0 0 169.201
## 4 transform.data 2 2 2 24.716
## 6 manage.missing.data 3 1 1 133.405
## end elapsed duration
## 11 459.616 199.450 199.450
## 14 721.652 180.100 180.100
## 5 133.404 108.380 108.379
## 12 532.107 72.492 72.491
## 10 260.165 71.646 71.646
## 7 169.201 35.654 35.654
## 16 761.557 30.211 30.211
## 9 188.519 18.489 18.489
## 1 18.808 9.875 9.875
## 15 731.345 9.693 9.693
## 13 541.552 9.444 9.444
## 2 23.653 4.844 4.844
## 3 24.715 1.061 1.061
## 8 170.029 0.829 0.828
## 4 25.024 0.308 0.308
## 6 133.547 0.142 0.142
## [1] "Total Elapsed Time: 761.557 secs"
## label step_major step_minor
## 10 fit.models_1_RFE.X 2 8
## 15 fit.models_1_Max.cor.Y.rcv.3X5.Interact 4 1
## 9 fit.models_1_RFE.X 2 7
## 13 fit.models_1_All.X 3 1
## 5 fit.models_1_RFE.X 2 3
## 11 fit.models_1_RFE.X 2 9
## 7 fit.models_1_RFE.X 2 5
## 8 fit.models_1_RFE.X 2 6
## 6 fit.models_1_RFE.X 2 4
## 4 fit.models_1_RFE.X 2 2
## 3 fit.models_1_RFE.X 2 1
## 14 fit.models_1_Best.Interact 4 0
## 1 fit.models_1_bgn 1 0
## 2 fit.models_1_RFE.X 2 0
## 12 fit.models_1_All.X 3 0
## label_minor bgn end elapsed duration
## 10 avNNet 343.291 393.098 49.808 49.807
## 15 glmnet 418.205 459.606 41.401 41.401
## 9 nnet 319.974 343.291 23.317 23.317
## 13 glmnet 403.123 418.177 15.054 15.054
## 5 glmnet 282.165 297.179 15.014 15.014
## 11 earth 393.099 403.115 10.016 10.016
## 7 gbm 303.642 312.614 8.972 8.972
## 8 rf 312.615 319.973 7.359 7.358
## 6 rpart 297.179 303.641 6.462 6.462
## 4 bayesglm 275.956 282.164 6.208 6.208
## 3 glm 270.418 275.956 5.538 5.538
## 14 setup 418.177 418.204 0.027 0.027
## 1 setup 270.399 270.409 0.010 0.010
## 2 setup 270.410 270.417 0.007 0.007
## 12 setup 403.116 403.123 0.007 0.007
## [1] "Total Elapsed Time: 459.606 secs"